diff --git a/README.md b/README.md
index 46662364..b3d2f0f0 100644
--- a/README.md
+++ b/README.md
@@ -79,7 +79,7 @@ Open Bandit Pipeline consists of the following main modules.
 
 - [**dataset module**](./obp/dataset/): This module provides a data loader for Open Bandit Dataset and a flexible interface for handling logged bandit data. It also provides tools to generate synthetic bandit data and transform multi-class classification data to bandit data.
 - [**policy module**](./obp/policy/): This module provides interfaces for implementing new online and offline bandit policies. It also implements several standard policy learning methods.
-- [**simulator module**](./obp/simulator/): This module provides functions for conducting offline bandit simulation. This module is necessary only when you use the ReplayMethod to evaluate online bandit policies. Please refer to [examples/quickstart/online.ipynb](./examples/quickstart/online.ipynb) for a quickstart guide of implementing OPE of online bandit algorithms.
+- [**simulator module**](./obp/simulator/): This module provides functions for conducting offline bandit simulation. This module is necessary only when you use the ReplayMethod to evaluate online bandit policies. Please refer to [examples/quickstart/online.ipynb](./examples/quickstart/replay.ipynb) for a quickstart guide of implementing OPE of online bandit algorithms.
 - [**ope module**](./obp/ope/): This module provides generic abstract interfaces to support custom implementations so that researchers can evaluate their own estimators easily. It also implements several basic and advanced OPE estimators.
 
 ### Supported Bandit Algorithms and OPE Estimators
diff --git a/examples/README.md b/examples/README.md
index 595998fe..b67a1597 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -5,6 +5,6 @@ This page contains a list of examples written with Open Bandit Pipeline.
 - [`obd/`](./obd/): example implementations for evaluating standard off-policy estimators with the small sample Open Bandit Dataset.
 - [`synthetic/`](./synthetic/): example implementations for evaluating several off-policy estimators with synthetic bandit datasets.
 - [`multiclass/`](./multiclass/): example implementations for evaluating several off-policy estimators with multi-class classification datasets.
-- [`online/`](./online/): example implementations for evaluating Replay Method with online bandit algorithms.
+- [`replay/`](./replay/): example implementations for evaluating Replay Method with online bandit algorithms.
 - [`opl/`](./opl/): example implementations for comparing the performance of several off-policy learners with synthetic bandit datasets.
 - [`quickstart/`](./quickstart/): some quickstart notebooks to guide the usage of Open Bandit Pipeline.
diff --git a/examples/quickstart/README.md b/examples/quickstart/README.md
index 6b45f932..53e1b111 100644
--- a/examples/quickstart/README.md
+++ b/examples/quickstart/README.md
@@ -5,6 +5,6 @@ This page contains a list of quickstart notebooks written with Open Bandit Pipel
 - [`obd.ipynb`](./obd.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/obd.ipynb): a quickstart guide of using Open Bandit Dataset and Pipeline to conduct some OPE experiments.
 - [`synthetic.ipynb`](./synthetic.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic.ipynb): a quickstart guide to implement the standard off-policy learning, OPE, and the evaluation of OPE on synthetic bandit data with Open Bandit Pipeline.
 - [`multiclass.ipynb`](./multiclass.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/multiclass.ipynb): a quickstart guide to handle multi-class classification data as logged bandit data for the standard off-policy learning, OPE, and the evaluation of OPE with Open Bandit Pipeline.
-- [`online.ipynb`](./online.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/online.ipynb): a quickstart guide to implement OPE and the evaluation of OPE for online bandit algorithms with Open Bandit Pipeline.
+- [`online.ipynb`](./replay.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/online.ipynb): a quickstart guide to implement OPE and the evaluation of OPE for online bandit algorithms with Open Bandit Pipeline.
 - [`opl.ipynb`](./opl.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/opl.ipynb): a quickstart guide to implement off-policy learners and the evaluation of off-policy learners with Open Bandit Pipeline.
 - [`synthetic_slate.ipynb`](./synthetic_slate.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic_slate.ipynb): a quickstart guide to implement OPE and the evaluation of OPE for the slate recommendation setting with Open Bandit Pipeline.
diff --git a/examples/quickstart/online-bandit-vs-opl-simulation.ipynb b/examples/quickstart/online-bandit-vs-opl-simulation.ipynb
new file mode 100644
index 00000000..89ec46ce
--- /dev/null
+++ b/examples/quickstart/online-bandit-vs-opl-simulation.ipynb
@@ -0,0 +1,1620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# On-policy bandit vs Greedy off-policy optimised policies\n",
+    "This notebook shows an empirical experiment comparing selection of bandit algorithms with off-policy optimised policies.\n",
+    "\n",
+    "We present two experiments that try to answer the following research questions\n",
+    "1. What is the effect of the number of rounds (training data) on the difference in reward of on-policy vs off-policy?\n",
+    "2. What is the difference between reward between different types of bandit algorithms when used as logged data for an Inverse Propensity Weighting (IPW) learner.\n",
+    "\n",
+    "Both experiments in this notebook have the following setup.\n",
+    "- The environment is stationary\n",
+    "- Feedback is instant (no delay)\n",
+    "- Bandit algorithms keep exploring / learning during the evaluation cycle\n",
+    "- Propensities for the IPW are learned by fitting a classifier predicting $P(A=a|c)$ on the training or evaluation dataset. Note that we fit a separate propensity model for the training and evaluation data and predict on the same dataset as it was trained on."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "from obp.policy.offline import IPWLearner\n",
+    "from obp.dataset import logistic_sparse_reward_function\n",
+    "from obp.policy import Random, EpsilonGreedy, BernoulliTS, LinTS\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier as RandomForest\n",
+    "\n",
+    "from obp.simulator.simulator import BanditEnvironmentSimulator, BanditPolicySimulator\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Off-policy vs on-policy at different number of rounds\n",
+    "In this section, we show how various bandit algorithms can be compared to an IPW learning given the amount of training and evaluation rounds."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4h 51min 1s, sys: 16min 43s, total: 5h 7min 44s\n",
+      "Wall time: 4h 41min 52s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "experiment_round_size = [1000, 10000, 100000, 500000, 1000000]\n",
+    "n_runs_per_round_size = 20\n",
+    "n_actions = 5\n",
+    "dim_context = 5\n",
+    "\n",
+    "bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinTS, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
+    "train_rewards = {policy.policy_name: [] for policy in dummy_policies}\n",
+    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []\n",
+    "\n",
+    "# In this setting the dataset is static, so we can use the same dataset class and redraw batches\n",
+    "# for different settings. When drift or delay is added the dataset object needs to be reinitiated with a new seed\n",
+    "# for each experiment.\n",
+    "env = BanditEnvironmentSimulator(\n",
+    "    n_actions=10,\n",
+    "    dim_context=5,\n",
+    "    reward_type=\"binary\", # \"binary\" or \"continuous\"\n",
+    "    reward_function=logistic_sparse_reward_function,\n",
+    "    random_state=12345,\n",
+    ")\n",
+    "\n",
+    "for n_rounds in experiment_round_size:\n",
+    "    for experiment in range(n_runs_per_round_size):\n",
+    "        training_bandit_batch = env.next_bandit_round_batch(n_rounds=n_rounds)\n",
+    "        evaluation_bandit_batch = env.next_bandit_round_batch(n_rounds=n_rounds)\n",
+    "\n",
+    "        # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "        for policy_class, args in bandit_policies:\n",
+    "            policy = policy_class(**args)\n",
+    "\n",
+    "            training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "\n",
+    "            training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "            train_rewards[policy.policy_name].append(training_simulator.total_reward)\n",
+    "\n",
+    "            eval_simulator = BanditPolicySimulator(policy=policy)\n",
+    "\n",
+    "            eval_simulator.steps(batch_bandit_rounds=evaluation_bandit_batch)\n",
+    "            eval_rewards[policy.policy_name].append(eval_simulator.total_reward)\n",
+    "\n",
+    "        # Train a propensity model on the actions in the training period to get propensities per round\n",
+    "        propensity_model = LogisticRegression(random_state=12345)\n",
+    "        propensity_model.fit(training_simulator.contexts, training_simulator.selected_actions)\n",
+    "        pscores = propensity_model.predict_proba(training_simulator.contexts)\n",
+    "\n",
+    "        # Train an IPW learning from the logged data and learned propensities\n",
+    "        ipw_learner = IPWLearner(n_actions=env.n_actions,\n",
+    "                                 base_classifier=RandomForest(n_estimators=30, min_samples_leaf=10, random_state=12345))\n",
+    "\n",
+    "        ipw_learner.fit(\n",
+    "            context=training_simulator.contexts,\n",
+    "            action=training_simulator.selected_actions,\n",
+    "            reward=training_simulator.obtained_rewards,\n",
+    "            pscore=np.choose(training_simulator.selected_actions, pscores.T)\n",
+    "        )\n",
+    "        eval_action_dists = ipw_learner.predict(\n",
+    "            context=eval_simulator.contexts\n",
+    "        )\n",
+    "\n",
+    "        eval_rewards[ipw_learner.policy_name].append(\n",
+    "            np.sum(eval_action_dists.squeeze(axis=2) * evaluation_bandit_batch.rewards)\n",
+    "        )\n",
+    "\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)\n",
+    "        eval_rewards[\"n_rounds\"].append(n_rounds)\n",
+    "\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The plot below compares the off-policy model to different on-policy bandit algorithms on the evaluation dataset.\n",
+    "\n",
+    "IPWLearning has been allowed to learn from n_rounds of samples from the logged training data, the other policies have been exploring on n_rounds and can now exploit (and learn) on the evaluation data.\n",
+    "\n",
+    "The plot compares the average reward per model. We clearly see that after $10^4$ IPWLearning start outperforming all on-policy models reaching an average reward of around .65 vs the highest on-policy reward of .56. Additionally, the on-policy models do not seem to increase their performance after $10^4$ rounds.\n",
+    "\n",
+    "This seems to indicate that using an off-policy method to optimise your bandit model can yield higher performance than continious learning with on-policy models."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_average_reward_per_n_rounds(rewards):\n",
+    "    rewards_pd = pd.DataFrame(rewards)\n",
+    "    rewards_pd = pd.melt(rewards_pd, ['n_rounds'])\n",
+    "    rewards_pd[\"average reward\"] = rewards_pd[\"value\"] / rewards_pd[\"n_rounds\"]\n",
+    "\n",
+    "    plot = sns.lineplot(data=rewards_pd, x=\"n_rounds\", y=\"average reward\", style=\"variable\", hue=\"variable\", markers=True, dashes=False)\n",
+    "    plot.set(xscale='log')\n",
+    "    plot.legend(bbox_to_anchor=(1.1, 1.05))\n",
+    "\n",
+    "plot_average_reward_per_n_rounds(eval_rewards)\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Off-policy on different kinds of bandit policy logs\n",
+    "Understanding how counterfactual examples affect the performance of various off-policy optimisers can be useful in deciding which strategy for data collection to take.\n",
+    "\n",
+    "In this next section, we run a bandit algorithm in order to collect logs that can be used for off-policy learning. These logs are used to train a propensity model for fitting a IPW model to be used for evaluation."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:00<00:00, 81137.15it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 58612.41it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 4992.29it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 51322.17it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 26147.07it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 25413.71it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 4780.59it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 96032.24it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 21132.66it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 54953.93it/s]\n",
+      "100%|██████████| 1000/1000 [00:00<00:00, 5120.64it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4h 53min 32s, sys: 54min 7s, total: 5h 47min 40s\n",
+      "Wall time: 4h 30min 31s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "experiment_round_size = [1000, 10000, 100000, 500000, 1000000]\n",
+    "n_runs_per_round_size = 20\n",
+    "n_actions = 5\n",
+    "dim_context = 5\n",
+    "\n",
+    "bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinTS, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
+    "train_rewards = {policy.policy_name: [] for policy in dummy_policies}\n",
+    "eval_rewards = {f\"{IPWLearner.__name__} {policy.policy_name}\": [] for policy in dummy_policies}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []\n",
+    "\n",
+    "# In this setting the dataset is static, so we can use the same dataset class and redraw batches\n",
+    "# for different settings. When drift or delay is added the dataset object needs to be reinitiated with a new seed\n",
+    "# for each experiment.\n",
+    "env = BanditEnvironmentSimulator(\n",
+    "    n_actions=10,\n",
+    "    dim_context=5,\n",
+    "    reward_type=\"binary\",\n",
+    "    reward_function=logistic_sparse_reward_function,\n",
+    "    random_state=12345,\n",
+    ")\n",
+    "\n",
+    "for n_rounds in experiment_round_size:\n",
+    "    for experiment in range(n_runs_per_round_size):\n",
+    "        training_bandit_batch = env.next_bandit_round_batch(n_rounds=n_rounds)\n",
+    "        evaluation_bandit_batch = env.next_bandit_round_batch(n_rounds=n_rounds)\n",
+    "\n",
+    "        # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "        for policy_class, args in bandit_policies:\n",
+    "            policy = policy_class(**args)\n",
+    "\n",
+    "            training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "\n",
+    "            training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "            train_rewards[policy.policy_name].append(training_simulator.total_reward)\n",
+    "\n",
+    "            # Train a propensity model on the actions in the training period to get propensities per round\n",
+    "            propensity_model = LogisticRegression(random_state=12345)\n",
+    "            propensity_model.fit(training_simulator.contexts, training_simulator.selected_actions)\n",
+    "            pscores = propensity_model.predict_proba(training_simulator.contexts)\n",
+    "\n",
+    "            # Train an IPW learning from the logged data and learned propensities\n",
+    "            ipw_learner = IPWLearner(n_actions=env.n_actions,\n",
+    "                                     base_classifier=RandomForest(n_estimators=30, min_samples_leaf=10, random_state=12345))\n",
+    "\n",
+    "            ipw_learner.fit(\n",
+    "                context=training_simulator.contexts,\n",
+    "                action=training_simulator.selected_actions,\n",
+    "                reward=training_simulator.obtained_rewards,\n",
+    "                pscore=np.choose(training_simulator.selected_actions, pscores.T)\n",
+    "            )\n",
+    "            eval_action_dists = ipw_learner.predict(\n",
+    "                context=evaluation_bandit_batch.context\n",
+    "            )\n",
+    "\n",
+    "            eval_rewards[f\"{ipw_learner.policy_name} {policy.policy_name}\"].append(\n",
+    "                np.sum(eval_action_dists.squeeze(axis=2) * evaluation_bandit_batch.rewards)\n",
+    "            )\n",
+    "\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)\n",
+    "        eval_rewards[\"n_rounds\"].append(n_rounds)\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The plot below shows how the performance of the same IPW model setup can significantly differ when the underlying data collection process changes."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_average_reward_per_n_rounds(eval_rewards)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/quickstart/online-bandit-with-delay-simulation.ipynb b/examples/quickstart/online-bandit-with-delay-simulation.ipynb
new file mode 100644
index 00000000..6b068438
--- /dev/null
+++ b/examples/quickstart/online-bandit-with-delay-simulation.ipynb
@@ -0,0 +1,6463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Delayed rewards for bandit algorithms\n",
+    "Literature on bandit algorithms frequently does not take into account that rewards are not observed instantly. In this notebook, we demonstrate the impact of this delayed feedback on known bandit algorithms using a simulation.\n",
+    "\n",
+    "The simulation works by sampling delay from an exponential distribution as described by *Ktena, Sofia Ira, et al.*[1]. In future experiments it would also be interesting to prior the distribution with the values in context $c$.\n",
+    "This delay is expressed in $d_i$ representing the amount of rounds the algorithm has to wait before being allowed to observe the reward.\n",
+    "\n",
+    "Key assumptions\n",
+    "- Delay is independent of the context, action and reward. In real-world applications there might be a connection between these.\n",
+    "- Feedback is never received in the same round as the prediction that is being made. In high volume traffic, it's highly unlikely that a user is able to respond to an action before the next user requests a prediction.\n",
+    "\n",
+    "Open questions:\n",
+    "- Should the model be aware of the delay it took for getting the reward?\n",
+    "- What is the default behaviour when actions are taken but rewards are not available yet?\n",
+    "\n",
+    "[1] [Ktena, Sofia Ira, et al. \"Addressing delayed feedback for continuous training with neural networks in CTR prediction.\" Proceedings of the 13th ACM conference on recommender systems. 2019.](https://arxiv.org/pdf/1907.06558.pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "from functools import partial\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from obp.simulator.coefficient_drifter import CoefficientDrifter\n",
+    "from obp.simulator.delay_sampler import ExponentialDelaySampler\n",
+    "from obp.simulator.simulator import BanditEnvironmentSimulator, BanditPolicySimulator\n",
+    "\n",
+    "from obp.policy.offline import IPWLearner\n",
+    "from obp.dataset import logistic_sparse_reward_function\n",
+    "from obp.policy import Random, EpsilonGreedy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "experiment_round_size = np.arange(start=1000, stop=20001, step=100).tolist()\n",
+    "\n",
+    "n_runs_per_round_size = 1000\n",
+    "max_scale = 1000.0\n",
+    "min_scale = 900.0\n",
+    "n_actions = 4\n",
+    "dim_context = 5\n",
+    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
+    "\n",
+    "bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
+    "train_rewards = [{policy.policy_name + f\" {type} delay\": [] for policy in dummy_policies} for type in [\"no\", \"RI\", \"RD\"]]\n",
+    "train_rewards = {k: v for d in train_rewards for k, v in d.items()}\n",
+    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get reward dependent delays & no delay data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1h 27min 19s, sys: 21min 27s, total: 1h 48min 46s\n",
+      "Wall time: 1h 18min 41s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "delay_function = ExponentialDelaySampler(min_scale=min_scale, max_scale=max_scale, random_state=12345).exponential_delay_function_expected_reward_weighted\n",
+    "\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\", # \"binary\" or \"continuous\"\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=delay_function,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    # print((np.arange(0, len(training_bandit_dataset[\"round_delays\"])) + training_bandit_dataset[\"round_delays\"] < sample_n_rounds).sum())\n",
+    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "    # Take into account all the delays during training\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" RD delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "    # Ignore delays during training\n",
+    "    training_bandit_batch.round_delays = None\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" no delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "    for n_rounds in experiment_round_size:\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1h 12min 4s, sys: 22min 2s, total: 1h 34min 6s\n",
+      "Wall time: 1h 3min 41s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    delay_function = ExponentialDelaySampler(max_scale=max_scale, random_state=12345).exponential_delay_function\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\", # \"binary\" or \"continuous\"\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=delay_function,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "    # Take into account all the delays during training\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" RI delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_average_reward_per_n_rounds(rewards):\n",
+    "    rewards_pd = pd.DataFrame(rewards)\n",
+    "    rewards_pd = pd.melt(rewards_pd, ['n_rounds'])\n",
+    "    rewards_pd[\"average reward\"] = rewards_pd[\"value\"] / rewards_pd[\"n_rounds\"]\n",
+    "\n",
+    "    plot = sns.lineplot(data=rewards_pd, x=\"n_rounds\", y=\"average reward\", style=\"variable\", hue=\"variable\", markers=True, dashes=False)\n",
+    "    plot.legend(bbox_to_anchor=(1.1, 1.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "del train_rewards[\"egreedy_1.0 RD delay\"]\n",
+    "del train_rewards[\"egreedy_1.0 RI delay\"]\n",
+    "\n",
+    "plot_average_reward_per_n_rounds(train_rewards)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Delay under constant drift"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "experiment_round_size = np.arange(start=1000, stop=20001, step=100).tolist()\n",
+    "\n",
+    "n_runs_per_round_size = 10000\n",
+    "max_scale = 5000.0\n",
+    "n_actions = 4\n",
+    "dim_context = 5\n",
+    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
+    "\n",
+    "bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in bandit_policies]\n",
+    "train_rewards = [{policy.policy_name + f\" {type} delay\": [] for policy in dummy_policies} for type in [\"drift + no\", \"drift +\", \"no drift +\"]]\n",
+    "train_rewards = {k: v for d in train_rewards for k, v in d.items()}\n",
+    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7h 49min 13s, sys: 7h 53min 49s, total: 15h 43min 2s\n",
+      "Wall time: 5h 54min 35s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%%capture\n",
+    "\n",
+    "delay_function = ExponentialDelaySampler(min_scale=min_scale, max_scale=max_scale, random_state=12345).exponential_delay_function\n",
+    "\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    drifter = CoefficientDrifter(\n",
+    "        drift_interval=5000,\n",
+    "        transition_period=100,\n",
+    "        transition_type=\"weighted_sampled\",  # linear or weighted_sampled\n",
+    "        seasonal=True,\n",
+    "        base_coefficient_weight=.3,\n",
+    "        random_state=1234 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        coef_function=drifter.get_coefficients,\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=delay_function,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "    # Take into account all the delays during training\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" drift + delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "    # Train the bandit algorithms and get the rewards for the training and evaluation period\n",
+    "    # Ignore delays during training\n",
+    "    training_bandit_batch.round_delays = None\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" drift + no delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=delay_function,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    for policy_class, args in bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" no drift + delay\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "\n",
+    "    for n_rounds in experiment_round_size:\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "del train_rewards[\"egreedy_1.0 drift + delay\"]\n",
+    "train_rewards[\"random drift\"] = train_rewards.pop(\"egreedy_1.0 drift + no delay\")\n",
+    "train_rewards[\"random no drift\"] = train_rewards.pop(\"egreedy_1.0 no drift + delay\")\n",
+    "\n",
+    "plot_average_reward_per_n_rounds(train_rewards)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/examples/quickstart/online-bandit-with-drift-simulation.ipynb b/examples/quickstart/online-bandit-with-drift-simulation.ipynb
new file mode 100644
index 00000000..6a8b074f
--- /dev/null
+++ b/examples/quickstart/online-bandit-with-drift-simulation.ipynb
@@ -0,0 +1,2507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### The effect of concept drift on bandit algorithms\n",
+    "Bandit algorithms commonly assume a stationary environment. However, concept drift can affect the environment when these algorithms are put into practise.\n",
+    "\n",
+    "In this notebook we show how various types of concept drift can be simulated using OBP.\n",
+    "\n",
+    "The OBP Drift implementation works by changing the coefficients that define $f(r|x)$. The coefficients and therefor drift can be controlled by the following parameters:\n",
+    "* Seasonality - When true, the drift will happen between two distinct sets of coefficients. When false, each instances of drift will generate a new set of coefficients\n",
+    "* Base coefficient - Which defines the contribution of a base coefficient to the drifting environment. Eg. setting the base coefficient to .3 will combine the coefficients by add 30% of a static coefficient set and 70% of the combination of drifting coefficients. This allows the model to have some base coefficients to learn.\n",
+    "* Transition period - The time in rounds it takes to transition between two sets of coefficients.\n",
+    "* Transition type - The type of transition being either linear or weighted_sampled. These correspond to the linear and gradual drifts as described by *Cavenaghi, Emanuele, et al.*[1] respectively..\n",
+    "\n",
+    "\n",
+    "The parameters in OBP can be used to simulate any combination of methods described by *Cavenaghi, Emanuele, et al.*[1] and more. It is also possible to combine drift with reward delay in order to create even more realistic environments. However, in this notebook we restrict ourselves to one example of seasonal and one example of non-seasonal drift both without any reward delay.\n",
+    "\n",
+    "\n",
+    "[1] [Cavenaghi, Emanuele, et al. \"Non stationary multi-armed bandit: Empirical evaluation of a new concept drift-aware algorithm.\" Entropy 23.3 (2021): 380.](https://pdfs.semanticscholar.org/dc0d/c6b836205f6d9ecfcb479e17036efe666ea9.pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "from functools import partial\n",
+    "\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "\n",
+    "from obp.policy.offline import IPWLearner\n",
+    "from obp.dataset import logistic_sparse_reward_function\n",
+    "from obp.policy import Random, EpsilonGreedy, BernoulliTS, LinTS, LinUCB\n",
+    "from obp.simulator.coefficient_drifter import CoefficientDrifter\n",
+    "from obp.simulator.simulator import BanditEnvironmentSimulator, BanditPolicySimulator"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Seasonal concept drift with small linear transition period.\n",
+    "In this section we demonstrate a seasonal drift with a short linear transition period."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "experiment_round_size = np.arange(start=1000, stop=50001, step=1000).tolist()\n",
+    "drift_interval = 5000\n",
+    "transition_period = 500\n",
+    "seasonal = True\n",
+    "n_runs_per_round_size = 1000\n",
+    "max_scale = 1000.0\n",
+    "min_scale = 900.0\n",
+    "n_actions = 3\n",
+    "dim_context = 5\n",
+    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
+    "\n",
+    "drift_bandit_policies = [\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinTS, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinUCB, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "no_drift_bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in drift_bandit_policies]\n",
+    "train_rewards = {policy.policy_name + f\" drift\": [] for policy in dummy_policies}\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in no_drift_bandit_policies]\n",
+    "train_rewards.update({policy.policy_name + f\" no drift\": [] for policy in dummy_policies})\n",
+    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 50000/50000 [00:00<00:00, 114733.89it/s]\n",
+      "100%|██████████| 50000/50000 [00:00<00:00, 65387.88it/s]\n",
+      "100%|██████████| 50000/50000 [00:06<00:00, 8028.09it/s]\n",
+      "100%|██████████| 50000/50000 [00:01<00:00, 26503.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.69 s, sys: 683 ms, total: 10.4 s\n",
+      "Wall time: 9.88 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    drifter = CoefficientDrifter(\n",
+    "        drift_interval=drift_interval,\n",
+    "        transition_period=transition_period,\n",
+    "        transition_type=\"linear\",  # linear or weighted_sampled\n",
+    "        seasonal=seasonal,\n",
+    "        base_coefficient_weight=.3,\n",
+    "        random_state=1234\n",
+    "    )\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\",\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=None,\n",
+    "        coef_function=drifter.get_coefficients,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.next_bandit_round_batch(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    for policy_class, args in drift_bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" drift\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "    for n_rounds in experiment_round_size:\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 32min 11s, sys: 21min 41s, total: 53min 52s\n",
+      "Wall time: 24min 31s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    drifter = CoefficientDrifter(\n",
+    "        drift_interval=drift_interval,\n",
+    "        transition_period=transition_period,\n",
+    "        transition_type=\"linear\",  # linear or weighted_sampled\n",
+    "        seasonal=seasonal,\n",
+    "        base_coefficient_weight=1.0,\n",
+    "        random_state=1234\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\",\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=None,\n",
+    "        coef_function=drifter.get_coefficients,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.obtain_batch_bandit_feedback(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    for policy_class, args in drift_bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" no drift\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_average_reward_per_n_rounds(rewards):\n",
+    "    rewards_pd = pd.DataFrame(rewards)\n",
+    "    rewards_pd = pd.melt(rewards_pd, ['n_rounds'])\n",
+    "    rewards_pd[\"value\"] = rewards_pd[\"value\"] / rewards_pd[\"n_rounds\"]\n",
+    "\n",
+    "    plot = sns.lineplot(data=rewards_pd, x=\"n_rounds\", y=\"value\", style=\"variable\", hue=\"variable\", markers=True, dashes=False)\n",
+    "    plot.legend(bbox_to_anchor=(1.1, 1.05))\n",
+    "\n",
+    "# 1000 runs, only with drift, all models.\n",
+    "# 25,000 interval, 50,000 steps. transition period of 5000. Non-seasonal.\n",
+    "# base-coef is 0.3\n",
+    "plot_average_reward_per_n_rounds(train_rewards)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### One-time drift with short linear transition period.\n",
+    "In this section we demonstrate a drift period with a short linear transition period half-way through its horizon."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "experiment_round_size = np.arange(start=1000, stop=50001, step=1000).tolist()\n",
+    "drift_interval = 25000\n",
+    "transition_period = 5000\n",
+    "seasonal = True\n",
+    "n_runs_per_round_size = 1000\n",
+    "max_scale = 1000.0\n",
+    "min_scale = 900.0\n",
+    "n_actions = 3\n",
+    "dim_context = 5\n",
+    "reward_func = partial(logistic_sparse_reward_function, z_score=False)\n",
+    "\n",
+    "drift_bandit_policies = [\n",
+    "    (EpsilonGreedy, {\"n_actions\": n_actions, \"epsilon\": 0.1, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinTS, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "    (LinUCB, {\"dim\": dim_context, \"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "no_drift_bandit_policies = [\n",
+    "    (Random, {\"n_actions\": n_actions, \"epsilon\": 1.0, \"random_state\": 12345}),\n",
+    "    (BernoulliTS, {\"n_actions\": n_actions, \"random_state\": 12345}),\n",
+    "]\n",
+    "\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in drift_bandit_policies]\n",
+    "train_rewards = {policy.policy_name + f\" drift\": [] for policy in dummy_policies}\n",
+    "dummy_policies = [policy_class(**args) for policy_class, args in no_drift_bandit_policies]\n",
+    "train_rewards.update({policy.policy_name + f\" no drift\": [] for policy in dummy_policies})\n",
+    "eval_rewards = {**copy.deepcopy(train_rewards), **{IPWLearner.__name__: []}}\n",
+    "train_rewards[\"n_rounds\"] = []\n",
+    "eval_rewards[\"n_rounds\"] = []"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    drifter = CoefficientDrifter(\n",
+    "        drift_interval=drift_interval,\n",
+    "        transition_period=transition_period,\n",
+    "        transition_type=\"linear\",\n",
+    "        seasonal=seasonal,\n",
+    "        base_coefficient_weight=.3,\n",
+    "        random_state=1234\n",
+    "    )\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\",\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=None,\n",
+    "        coef_function=drifter.get_coefficients,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.obtain_batch_bandit_feedback(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    for policy_class, args in drift_bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" drift\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "\n",
+    "    for n_rounds in experiment_round_size:\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "for experiment in range(n_runs_per_round_size):\n",
+    "    drifter = CoefficientDrifter(\n",
+    "        drift_interval=drift_interval,\n",
+    "        transition_period=transition_period,\n",
+    "        transition_type=\"linear\",\n",
+    "        seasonal=seasonal,\n",
+    "        base_coefficient_weight=.3,\n",
+    "        random_state=1234\n",
+    "    )\n",
+    "\n",
+    "    env = BanditEnvironmentSimulator(\n",
+    "        n_actions=n_actions,\n",
+    "        dim_context=dim_context,\n",
+    "        reward_type=\"binary\",\n",
+    "        reward_function=reward_func,\n",
+    "        delay_function=None,\n",
+    "        coef_function=drifter.get_coefficients,\n",
+    "        random_state=12345 + experiment,\n",
+    "    )\n",
+    "\n",
+    "    sample_n_rounds = max(experiment_round_size)\n",
+    "    training_bandit_batch = env.obtain_batch_bandit_feedback(n_rounds=sample_n_rounds)\n",
+    "\n",
+    "    for policy_class, args in drift_bandit_policies:\n",
+    "        policy = policy_class(**args)\n",
+    "        training_simulator = BanditPolicySimulator(policy=policy)\n",
+    "        training_simulator.steps(batch_bandit_rounds=training_bandit_batch)\n",
+    "\n",
+    "        for n_rounds in experiment_round_size:\n",
+    "            train_rewards[policy.policy_name + \" drift\"].append(np.sum(training_simulator.obtained_rewards[:n_rounds]))\n",
+    "\n",
+    "\n",
+    "    for n_rounds in experiment_round_size:\n",
+    "        train_rewards[\"n_rounds\"].append(n_rounds)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_average_reward_per_n_rounds(rewards):\n",
+    "    rewards_pd = pd.DataFrame(rewards)\n",
+    "    rewards_pd = pd.melt(rewards_pd, ['n_rounds'])\n",
+    "    rewards_pd[\"value\"] = rewards_pd[\"value\"] / rewards_pd[\"n_rounds\"]\n",
+    "\n",
+    "    plot = sns.lineplot(data=rewards_pd, x=\"n_rounds\", y=\"value\", style=\"variable\", hue=\"variable\", markers=True, dashes=False)\n",
+    "    plot.legend(bbox_to_anchor=(1.1, 1.05))\n",
+    "\n",
+    "# 1000 runs, all models with drift, bts and random without drift.\n",
+    "# 25,000 interval, 50,000 steps. transition period of 5000. Non-seasonal.\n",
+    "# base-coef is 0.3\n",
+    "# All models are heavily hit by the drift event and do not recover within the same time period as the model was able to learn before.\n",
+    "# However, all models still perform signicantly better than random due to the base coef being the same.\n",
+    "plot_average_reward_per_n_rounds(train_rewards)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/quickstart/online.ipynb b/examples/quickstart/replay.ipynb
similarity index 99%
rename from examples/quickstart/online.ipynb
rename to examples/quickstart/replay.ipynb
index 057bdf53..0a600832 100644
--- a/examples/quickstart/online.ipynb
+++ b/examples/quickstart/replay.ipynb
@@ -49,7 +49,6 @@
     ")\n",
     "from obp.simulator import (\n",
     "    calc_ground_truth_policy_value,\n",
-    "    run_bandit_simulation\n",
     ")"
    ]
   },
@@ -253,7 +252,7 @@
     "    epsilon=0.1,\n",
     "    random_state=12345\n",
     ")\n",
-    "action_dist_epsilon_greedy = run_bandit_simulation(\n",
+    "action_dist_epsilon_greedy = run_bandit_replay(\n",
     "    bandit_feedback=bandit_feedback,\n",
     "    policy=epsilon_greedy\n",
     ")\n",
@@ -263,7 +262,7 @@
     "    n_actions=dataset.n_actions,\n",
     "    random_state=12345\n",
     ")\n",
-    "action_dist_lin_ts = run_bandit_simulation(\n",
+    "action_dist_lin_ts = run_bandit_replay(\n",
     "    bandit_feedback=bandit_feedback,\n",
     "    policy=lin_ts\n",
     ")\n",
@@ -273,7 +272,7 @@
     "    n_actions=dataset.n_actions,\n",
     "    random_state=12345\n",
     ")\n",
-    "action_dist_lin_ucb = run_bandit_simulation(\n",
+    "action_dist_lin_ucb = run_bandit_replay(\n",
     "    bandit_feedback=bandit_feedback,\n",
     "    policy=lin_ucb\n",
     ")"
@@ -741,4 +740,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/examples/online/README.md b/examples/replay/README.md
similarity index 98%
rename from examples/online/README.md
rename to examples/replay/README.md
index 2fab3a71..fe249013 100644
--- a/examples/online/README.md
+++ b/examples/replay/README.md
@@ -1,4 +1,4 @@
-# Example with Online Bandit Algorithms
+# Replay Example with Online Bandit Algorithms
 
 
 ## Description
diff --git a/examples/online/evaluate_off_policy_estimators.py b/examples/replay/evaluate_off_policy_estimators.py
similarity index 97%
rename from examples/online/evaluate_off_policy_estimators.py
rename to examples/replay/evaluate_off_policy_estimators.py
index 80c72005..540824a9 100644
--- a/examples/online/evaluate_off_policy_estimators.py
+++ b/examples/replay/evaluate_off_policy_estimators.py
@@ -19,14 +19,13 @@
 from obp.policy import LogisticTS
 from obp.policy import LogisticUCB
 from obp.simulator import calc_ground_truth_policy_value
-from obp.simulator import run_bandit_simulation
-
+from obp.utils import run_bandit_replay
 
 ope_estimators = [ReplayMethod()]
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="evaluate off-policy estimators with online bandit algorithms and synthetic bandit data."
+        description="evaluate off-policy estimators with replay bandit algorithms and synthetic bandit data."
     )
     parser.add_argument(
         "--n_runs", type=int, default=1, help="number of simulations in the experiment."
@@ -127,7 +126,7 @@ def process(i: int):
         # sample new data of synthetic logged bandit feedback
         bandit_feedback = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)
         # simulate the evaluation policy
-        action_dist = run_bandit_simulation(
+        action_dist = run_bandit_replay(
             bandit_feedback=bandit_feedback, policy=evaluation_policy
         )
         # estimate the ground-truth policy values of the evaluation policy
diff --git a/obp/dataset/synthetic.py b/obp/dataset/synthetic.py
index 939eebbe..f1f02327 100644
--- a/obp/dataset/synthetic.py
+++ b/obp/dataset/synthetic.py
@@ -3,7 +3,7 @@
 
 """Class for Generating Synthetic Logged Bandit Data."""
 from dataclasses import dataclass
-from typing import Callable
+from typing import Callable, Tuple
 from typing import Optional
 
 import numpy as np
@@ -20,6 +20,25 @@
 from .base import BaseBanditDataset
 from .reward_type import RewardType
 
+coef_func_signature = Callable[
+    [np.ndarray, np.ndarray, np.random.RandomState],
+    Tuple[np.ndarray, np.ndarray, np.ndarray],
+]
+
+
+def sample_random_uniform_coefficients(
+    effective_dim_action_context: int,
+    effective_dim_context: int,
+    random_: np.random.RandomState,
+    **kwargs,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    context_coef_ = random_.uniform(-1, 1, size=effective_dim_context)
+    action_coef_ = random_.uniform(-1, 1, size=effective_dim_action_context)
+    context_action_coef_ = random_.uniform(
+        -1, 1, size=(effective_dim_context, effective_dim_action_context)
+    )
+    return context_coef_, action_coef_, context_action_coef_
+
 
 @dataclass
 class SyntheticBanditDataset(BaseBanditDataset):
@@ -398,6 +417,8 @@ def calc_ground_truth_policy_value(
 def logistic_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Logistic mean reward function for binary rewards.
@@ -410,6 +431,13 @@ def logistic_reward_function(
     action_context: array-like, shape (n_actions, dim_action_context)
         Vector representation of actions.
 
+    z_score: boolean, default=True
+        Controls whether a z-score will be calculated over the computed logits.
+
+    coef_function: Callable, default=sample_random_uniform_coefficients
+        Function for generating the coefficients used for the context, action and context/action interactions.
+        By default, the coefficients are randomly uniformly drawn.
+
     random_state: int, default=None
         Controls the random seed in sampling dataset.
 
@@ -425,6 +453,8 @@ def logistic_reward_function(
         action_context=action_context,
         degree=1,
         random_state=random_state,
+        z_score=z_score,
+        coef_function=coef_function,
     )
 
     return sigmoid(logits)
@@ -433,6 +463,8 @@ def logistic_reward_function(
 def logistic_polynomial_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Logistic mean reward function for binary rewards with polynomial feature transformations.
@@ -450,6 +482,9 @@ def logistic_polynomial_reward_function(
     action_context: array-like, shape (n_actions, dim_action_context)
         Vector representation of actions.
 
+    z_score: boolean, default=True
+        Controls whether a z-score will be calculated over the computed logits.
+
     random_state: int, default=None
         Controls the random seed in sampling dataset.
 
@@ -464,6 +499,8 @@ def logistic_polynomial_reward_function(
         context=context,
         action_context=action_context,
         degree=3,
+        coef_function=coef_function,
+        z_score=z_score,
         random_state=random_state,
     )
 
@@ -473,6 +510,8 @@ def logistic_polynomial_reward_function(
 def logistic_sparse_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Logistic mean reward function for binary rewards with small effective feature dimension.
@@ -491,6 +530,9 @@ def logistic_sparse_reward_function(
     action_context: array-like, shape (n_actions, dim_action_context)
         Vector representation of actions.
 
+    z_score: boolean, default=True
+        Controls whether a z-score will be calculated over the computed logits.
+
     random_state: int, default=None
         Controls the random seed in sampling dataset.
 
@@ -506,6 +548,8 @@ def logistic_sparse_reward_function(
         action_context=action_context,
         degree=4,
         effective_dim_ratio=0.3,
+        coef_function=coef_function,
+        z_score=z_score,
         random_state=random_state,
     )
 
@@ -515,6 +559,8 @@ def logistic_sparse_reward_function(
 def linear_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Linear mean reward function for continuous rewards.
@@ -527,6 +573,9 @@ def linear_reward_function(
     action_context: array-like, shape (n_actions, dim_action_context)
         Vector representation of actions.
 
+    z_score: boolean, default=True
+        Controls whether a z-score will be calculated over the computed logits.
+
     random_state: int, default=None
         Controls the random seed in sampling dataset.
 
@@ -541,6 +590,8 @@ def linear_reward_function(
         context=context,
         action_context=action_context,
         degree=1,
+        coef_function=coef_function,
+        z_score=z_score,
         random_state=random_state,
     )
 
@@ -548,6 +599,8 @@ def linear_reward_function(
 def polynomial_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Polynomial mean reward function for continuous rewards.
@@ -579,6 +632,8 @@ def polynomial_reward_function(
         context=context,
         action_context=action_context,
         degree=3,
+        coef_function=coef_function,
+        z_score=z_score,
         random_state=random_state,
     )
 
@@ -586,6 +641,8 @@ def polynomial_reward_function(
 def sparse_reward_function(
     context: np.ndarray,
     action_context: np.ndarray,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Sparse mean reward function for continuous rewards.
@@ -619,6 +676,8 @@ def sparse_reward_function(
         action_context=action_context,
         degree=4,
         effective_dim_ratio=0.3,
+        coef_function=coef_function,
+        z_score=z_score,
         random_state=random_state,
     )
 
@@ -628,6 +687,8 @@ def _base_reward_function(
     action_context: np.ndarray,
     degree: int = 3,
     effective_dim_ratio: float = 1.0,
+    coef_function: coef_func_signature = sample_random_uniform_coefficients,
+    z_score: bool = True,
     random_state: Optional[int] = None,
 ) -> np.ndarray:
     """Base function to define mean reward functions.
@@ -678,6 +739,10 @@ def _base_reward_function(
         only `dim_context * effective_dim_ratio` fraction of randomly selected dimensions
         will be used as relevant dimensions to generate expected rewards.
 
+    z_score: boolean, default=True
+        Boolean to enable/disable the calculation of a z-score over the resulting rewards. In case the environment
+        is stationary, this can be turned on. But when the
+
     random_state: int, default=None
         Controls the random seed in sampling dataset.
 
@@ -724,21 +789,45 @@ def _base_reward_function(
         effective_context_ = context_
         effective_action_context_ = action_context_
 
-    context_coef_ = random_.uniform(-1, 1, size=effective_dim_context)
-    action_coef_ = random_.uniform(-1, 1, size=effective_dim_action_context)
-    context_action_coef_ = random_.uniform(
-        -1, 1, size=(effective_dim_context, effective_dim_action_context)
+    context_coef_, action_coef_, context_action_coef_ = coef_function(
+        n_rounds=datasize,
+        effective_dim_action_context=effective_dim_action_context,
+        effective_dim_context=effective_dim_context,
+        random_=random_,
     )
 
-    context_values = np.tile(effective_context_ @ context_coef_, (n_actions, 1)).T
-    action_values = np.tile(action_coef_ @ effective_action_context_.T, (datasize, 1))
-    context_action_values = (
-        effective_context_ @ context_action_coef_ @ effective_action_context_.T
-    )
+    if context_coef_.shape[0] != datasize:
+        context_values = np.tile(effective_context_ @ context_coef_, (n_actions, 1)).T
+    else:
+        context_values = np.tile(
+            np.sum(effective_context_ * context_coef_, axis=1), (n_actions, 1)
+        ).T
+
+    action_values = action_coef_ @ effective_action_context_.T
+    if action_coef_.shape[0] != datasize:
+        action_values = np.tile(action_values, (datasize, 1))
+
+    if action_coef_.shape[0] != datasize:
+        context_action_values = (
+            effective_context_ @ context_action_coef_ @ effective_action_context_.T
+        )
+    else:
+        effective_context_ = np.expand_dims(effective_context_, axis=1)
+        context_action_coef_interactions = np.squeeze(
+            np.matmul(effective_context_, context_action_coef_), axis=1
+        )
+
+        context_action_values = (
+            context_action_coef_interactions @ effective_action_context_.T
+        )
+
     expected_rewards = context_values + action_values + context_action_values
-    expected_rewards = (
-        degree * (expected_rewards - expected_rewards.mean()) / expected_rewards.std()
-    )
+    if z_score:
+        expected_rewards = (
+            expected_rewards - expected_rewards.mean() / expected_rewards.std()
+        )
+
+    expected_rewards = degree * expected_rewards
 
     return expected_rewards
 
diff --git a/obp/policy/offline.py b/obp/policy/offline.py
index 21d56ccc..21041acf 100644
--- a/obp/policy/offline.py
+++ b/obp/policy/offline.py
@@ -72,6 +72,7 @@ def __post_init__(self) -> None:
         self.base_classifier_list = [
             clone(self.base_classifier) for _ in np.arange(self.len_list)
         ]
+        self.policy_name = "IPWLearner"
 
     @staticmethod
     def _create_train_data_for_opl(
diff --git a/obp/simulator/__init__.py b/obp/simulator/__init__.py
index 362971fb..447ff5f6 100644
--- a/obp/simulator/__init__.py
+++ b/obp/simulator/__init__.py
@@ -1,8 +1,6 @@
 from obp.simulator.simulator import calc_ground_truth_policy_value
-from obp.simulator.simulator import run_bandit_simulation
 
 
 __all__ = [
-    "run_bandit_simulation",
     "calc_ground_truth_policy_value",
 ]
diff --git a/obp/simulator/coefficient_drifter.py b/obp/simulator/coefficient_drifter.py
new file mode 100644
index 00000000..75cba871
--- /dev/null
+++ b/obp/simulator/coefficient_drifter.py
@@ -0,0 +1,240 @@
+from collections import deque
+from dataclasses import dataclass
+from typing import Optional, Tuple, List
+
+import numpy as np
+from sklearn.utils import check_random_state
+
+from obp.dataset.synthetic import sample_random_uniform_coefficients
+
+
+@dataclass
+class CoefficientDrifter:
+    """Class for synthesizing bandit data.
+
+    Note
+    -----
+    By calling the `obtain_batch_bandit_feedback` method several times,
+    we can resample logged bandit data from the same data generating distribution.
+    This can be used to estimate confidence intervals of the performances of OPE estimators.
+
+    If None is given as `behavior_policy_function`, the behavior policy will be generated from the true expected reward function. See the description of the `beta` argument, which controls the behavior policy.
+
+    Parameters
+    -----------
+    drift_interval: int
+        Controls interval of steps at which the coefficients are updated.
+
+    transition_period: int, default=0
+        Controls the period in which the coefficients are transitioning between new and old. The transition period
+        always happened before the drift interval. Meaning, that if the drift interval is 5000 and the transition period
+        500, the transition will be between step 4500 and step 5000.
+
+    transition_type: str, default="linear"
+        The type of transition (linear or weighted_sampled) to be applied while transitioning between two sets of
+        coefficients.
+
+    seasonal: bool, default=False
+        When True, the coefficients will shift between two sets of coefficients representing a seasonal shift.
+
+    base_coefficient_weight: float, default=0.0
+        A floating point between 0.0 and 1.0 that represents a base coefficient weight that is consistent regardless of
+        any drift. This can be used to ensure the severity of the drift over time.
+
+    effective_dim_action_context: (optional) int, default=None
+        Specifies the dimensions of the action context coefficients.
+
+    effective_dim_context: (optional) int, default=None
+        Specifies the dimensions of the context coefficients.
+
+    random_state: int, default=12345
+        Controls the random seed
+
+    References
+    ------------
+    Emanuele Cavenaghi, Gabriele Sottocornola, Fabio Stella, and Markus Zanker.
+    "Non stationary multi-armed bandit: Empirical evaluation of a new concept drift-aware algorithm.", 2021.
+
+    """
+
+    drift_interval: int
+    transition_period: int = 0
+    transition_type: str = "linear"  # linear or weighted_sampled
+    seasonal: bool = False
+    base_coefficient_weight: float = 0.0
+    effective_dim_action_context: Optional[int] = None
+    effective_dim_context: Optional[int] = None
+    random_state: int = 12345
+
+    played_rounds: int = 0
+    context_coefs: Optional[deque] = None
+    action_coefs: Optional[deque] = None
+    context_action_coefs: Optional[deque] = None
+    base_context_coef: Optional[np.ndarray] = None
+    base_action_coef: Optional[np.ndarray] = None
+    base_context_action_coef: Optional[np.ndarray] = None
+
+    def __post_init__(self) -> None:
+        if self.random_state is None:
+            raise ValueError("`random_state` must be given")
+        self.random_ = check_random_state(self.random_state)
+        self.available_rounds = self.drift_interval
+        self.context_coefs = deque(maxlen=2)
+        self.action_coefs = deque(maxlen=2)
+        self.context_action_coefs = deque(maxlen=2)
+        if self.effective_dim_action_context and self.effective_dim_context:
+            self.update_coef()
+
+    def update_coef(self) -> None:
+        if self.base_context_coef is None:
+            (
+                self.base_context_coef,
+                self.base_action_coef,
+                self.base_context_action_coef,
+            ) = sample_random_uniform_coefficients(
+                self.effective_dim_action_context,
+                self.effective_dim_context,
+                self.random_,
+            )
+
+        if len(self.context_coefs) == 0:
+            self.context_coefs.append(self.base_context_coef)
+            self.action_coefs.append(self.base_action_coef)
+            self.context_action_coefs.append(self.base_context_action_coef)
+
+        if self.seasonal and len(self.context_coefs) == 2:
+            self.context_coefs.rotate()
+            self.action_coefs.rotate()
+            self.context_action_coefs.rotate()
+        else:
+            (
+                tmp_context_coef,
+                tmp_action_coef,
+                tmp_action_context_coef,
+            ) = sample_random_uniform_coefficients(
+                self.effective_dim_action_context,
+                self.effective_dim_context,
+                self.random_,
+            )
+            self.context_coefs.append(tmp_context_coef)
+            self.action_coefs.append(tmp_action_coef)
+            self.context_action_coefs.append(tmp_action_context_coef)
+
+    def get_coefficients(
+        self,
+        n_rounds: int,
+        effective_dim_context: int = None,
+        effective_dim_action_context: int = None,
+        **kwargs,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if effective_dim_action_context and effective_dim_context:
+            eff_dim_not_set = (
+                not self.effective_dim_action_context and not self.effective_dim_context
+            )
+            eff_dim_equal = (
+                self.effective_dim_action_context == effective_dim_action_context
+                and self.effective_dim_context == effective_dim_context
+            )
+            if eff_dim_not_set or eff_dim_equal:
+                self.effective_dim_action_context = effective_dim_action_context
+                self.effective_dim_context = effective_dim_context
+            else:
+                raise RuntimeError("Trying to change the effective dimensions")
+
+        if len(self.context_coefs) == 0:
+            self.update_coef()
+
+        required_rounds = n_rounds
+        context_coefs = []
+        action_coefs = []
+        context_action_coefs = []
+
+        while required_rounds > 0:
+            if required_rounds >= self.available_rounds:
+                self.append_current_coefs(
+                    context_coefs,
+                    action_coefs,
+                    context_action_coefs,
+                    rounds=self.available_rounds,
+                )
+                required_rounds -= self.available_rounds
+                self.update_coef()
+                self.available_rounds = self.drift_interval
+            else:
+                self.append_current_coefs(
+                    context_coefs,
+                    action_coefs,
+                    context_action_coefs,
+                    rounds=required_rounds,
+                )
+                self.available_rounds -= required_rounds
+                required_rounds = 0
+
+        return (
+            np.vstack(context_coefs),
+            np.vstack(action_coefs),
+            np.vstack(context_action_coefs),
+        )
+
+    def append_current_coefs(
+        self,
+        context_coefs: List[np.ndarray],
+        action_coefs: List[np.ndarray],
+        context_action_coefs: List[np.ndarray],
+        rounds: int,
+    ) -> None:
+        shift_start = self.available_rounds - self.transition_period
+
+        transition_steps = np.arange(start=1, stop=self.transition_period + 1)
+        if shift_start >= 0:
+            transition_steps = np.pad(transition_steps, pad_width=[(shift_start, 0)])
+        if shift_start < 0:
+            transition_steps = transition_steps[-shift_start:]
+
+        shift_remainder = self.available_rounds - rounds
+        if shift_remainder > 0:
+            transition_steps = transition_steps[shift_remainder:]
+
+        weights = transition_steps / (self.transition_period + 1)
+
+        if self.transition_type == "weighted_sampled":
+            weights = self.random_.binomial(n=1, p=weights)
+
+        context_coefs.append(
+            self.compute_weighted_coefs(
+                self.context_coefs, self.base_context_coef, rounds, weights
+            )
+        )
+        action_coefs.append(
+            self.compute_weighted_coefs(
+                self.action_coefs, self.base_action_coef, rounds, weights
+            )
+        )
+        context_action_coefs.append(
+            self.compute_weighted_coefs(
+                self.context_action_coefs,
+                self.base_context_action_coef,
+                rounds,
+                weights,
+            )
+        )
+
+    def compute_weighted_coefs(self, coefs, base_coef, rounds, weights):
+        base_coef = self.base_coefficient_weight * base_coef
+
+        A = np.tile(coefs[0], [rounds] + [1 for _ in coefs[0].shape])
+        B = np.tile(coefs[1], [rounds] + [1 for _ in coefs[1].shape])
+        coefs = (
+            base_coef
+            + A
+            * np.expand_dims(
+                (1 - self.base_coefficient_weight) * (1 - weights),
+                list(range(1, len(A.shape))),
+            )
+            + B
+            * np.expand_dims(
+                (1 - self.base_coefficient_weight) * weights,
+                list(range(1, len(B.shape))),
+            )
+        )
+        return coefs
diff --git a/obp/simulator/delay_sampler.py b/obp/simulator/delay_sampler.py
new file mode 100644
index 00000000..49ebc3d7
--- /dev/null
+++ b/obp/simulator/delay_sampler.py
@@ -0,0 +1,96 @@
+from dataclasses import dataclass
+
+import numpy as np
+from sklearn.utils import check_random_state
+
+
+@dataclass
+class ExponentialDelaySampler:
+    """Class for sampling delays from different exponential functions.
+
+    Parameters
+    -----------
+    max_scale: float, default=100.0
+        The maximum scale parameter for the exponential delay distribution. When there is no weighted exponential
+        function the max_scale becomes the default scale.
+
+    min_scale: float, default=10.0
+        The minimum scale parameter for the exponential delay distribution. Only used when sampling from a weighted
+        exponential function.
+
+    random_state: int, default=12345
+        Controls the random seed in sampling synthetic bandit data.
+    """
+
+    max_scale: float = 100.0
+    min_scale: float = 10.0
+    random_state: int = None
+
+    def __post_init__(self) -> None:
+        if self.random_state is None:
+            raise ValueError("`random_state` must be given")
+        self.random_ = check_random_state(self.random_state)
+
+    def exponential_delay_function(
+        self, n_rounds: int, n_actions: int, **kwargs
+    ) -> np.ndarray:
+        """Exponential delay function used for sampling a number of delay rounds before rewards can be observed.
+
+        Note
+        ------
+        This implementation of the exponential delay function assumes that there is no causal relationship between the
+        context, action or reward and observed delay. Exponential delay function have been observed by Ktena, S.I. et al.
+
+        Parameters
+        -----------
+        n_rounds: int
+            Number of rounds to sample delays for.
+
+        n_actions: int
+            Number of actions to sample delays for. If the exponential function is not parameterised the delays are
+            repeated for each actions.
+
+        Returns
+        ---------
+        delay_rounds: array-like, shape (n_rounds, )
+            Rounded up round delays representing the amount of rounds before the policy can observe the rewards.
+
+        References
+        ------------
+        Ktena, S.I., Tejani, A., Theis, L., Myana, P.K., Dilipkumar, D., Huszár, F., Yoo, S. and Shi, W.
+        "Addressing delayed feedback for continuous training with neural networks in CTR prediction." 2019.
+
+        """
+        delays_per_round = np.ceil(
+            self.random_.exponential(scale=self.max_scale, size=n_rounds)
+        )
+
+        return np.tile(delays_per_round, (n_actions, 1)).T
+
+    def exponential_delay_function_expected_reward_weighted(
+        self, expected_rewards: np.ndarray, **kwargs
+    ) -> np.ndarray:
+        """Exponential delay function used for sampling a number of delay rounds before rewards can be observed.
+        Each delay is conditioned on the expected reward by multiplying (1 - expected_reward) * scale. This creates
+        the assumption that the more likely a reward is going be observed, the more likely it will be that the reward
+        comes sooner. Eg. recommending an attractive item will likely result in a faster purchase.
+
+         Parameters
+         -----------
+         expected_rewards : array-like, shape (n_rounds, n_actions)
+             The expected reward between 0 and 1 for each arm for each round. This used to weight the scale of the
+             exponential function.
+
+         Returns
+         ---------
+         delay_rounds: array-like, shape (n_rounds, )
+             Rounded up round delays representing the amount of rounds before the policy can observe the rewards.
+        """
+        scale = self.min_scale + (
+            (1 - expected_rewards) * (self.max_scale - self.min_scale)
+        )
+        delays_per_round = np.ceil(
+            self.random_.exponential(scale=scale, size=expected_rewards.shape)
+        )
+
+        return delays_per_round
diff --git a/obp/simulator/replay.py b/obp/simulator/replay.py
new file mode 100644
index 00000000..dd8fc53f
--- /dev/null
+++ b/obp/simulator/replay.py
@@ -0,0 +1,82 @@
+import numpy as np
+import tqdm as tqdm
+
+from obp.policy.policy_type import PolicyType
+from obp.simulator.simulator import BanditPolicy
+from obp.types import BanditFeedback
+from obp.utils import check_bandit_feedback_inputs, convert_to_action_dist
+
+
+def run_bandit_replay(
+    bandit_feedback: BanditFeedback, policy: BanditPolicy
+) -> np.ndarray:
+    """Run an online bandit algorithm on given logged bandit feedback data using the replay method.
+
+    Parameters
+    ----------
+    bandit_feedback: BanditFeedback
+        Logged bandit data used in offline bandit simulation.
+    policy: BanditPolicy
+        Online bandit policy to be evaluated in offline bandit simulation (i.e., evaluation policy).
+    Returns
+    --------
+    action_dist: array-like, shape (n_rounds, n_actions, len_list)
+        Action choice probabilities (can be deterministic).
+
+    References
+    ------------
+    Lihong Li, Wei Chu, John Langford, and Xuanhui Wang.
+    "Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms.", 2011.
+    """
+    for key_ in ["action", "position", "reward", "pscore", "context"]:
+        if key_ not in bandit_feedback:
+            raise RuntimeError(f"Missing key of {key_} in 'bandit_feedback'.")
+    check_bandit_feedback_inputs(
+        context=bandit_feedback["context"],
+        action=bandit_feedback["action"],
+        reward=bandit_feedback["reward"],
+        position=bandit_feedback["position"],
+        pscore=bandit_feedback["pscore"],
+    )
+
+    policy_ = policy
+    selected_actions_list = list()
+    dim_context = bandit_feedback["context"].shape[1]
+    if bandit_feedback["position"] is None:
+        bandit_feedback["position"] = np.zeros_like(
+            bandit_feedback["action"], dtype=int
+        )
+    for action_, reward_, position_, context_ in tqdm(
+        zip(
+            bandit_feedback["action"],
+            bandit_feedback["reward"],
+            bandit_feedback["position"],
+            bandit_feedback["context"],
+        ),
+        total=bandit_feedback["n_rounds"],
+    ):
+
+        # select a list of actions
+        if policy_.policy_type == PolicyType.CONTEXT_FREE:
+            selected_actions = policy_.select_action()
+        elif policy_.policy_type == PolicyType.CONTEXTUAL:
+            selected_actions = policy_.select_action(context_.reshape(1, dim_context))
+        action_match_ = action_ == selected_actions[position_]
+        # update parameters of a bandit policy
+        # only when selected actions&positions are equal to logged actions&positions
+        if action_match_:
+            if policy_.policy_type == PolicyType.CONTEXT_FREE:
+                policy_.update_params(action=action_, reward=reward_)
+            elif policy_.policy_type == PolicyType.CONTEXTUAL:
+                policy_.update_params(
+                    action=action_,
+                    reward=reward_,
+                    context=context_.reshape(1, dim_context),
+                )
+        selected_actions_list.append(selected_actions)
+
+    action_dist = convert_to_action_dist(
+        n_actions=bandit_feedback["action"].max() + 1,
+        selected_actions=np.array(selected_actions_list),
+    )
+    return action_dist
diff --git a/obp/simulator/simulator.py b/obp/simulator/simulator.py
index 2b2709fe..6f27b4dc 100755
--- a/obp/simulator/simulator.py
+++ b/obp/simulator/simulator.py
@@ -2,96 +2,432 @@
 # Licensed under the Apache 2.0 License.
 
 """Bandit Simulator."""
+from collections import defaultdict
 from copy import deepcopy
-from typing import Callable
+from dataclasses import dataclass
+from typing import Callable, Tuple, Optional, List, Any
 from typing import Union
 
 import numpy as np
+from sklearn.utils import check_random_state, check_scalar
 from tqdm import tqdm
 
+from ..dataset.reward_type import RewardType
+from ..dataset.synthetic import sample_random_uniform_coefficients
 from ..policy import BaseContextFreePolicy
 from ..policy import BaseContextualPolicy
 from ..policy.policy_type import PolicyType
 from ..types import BanditFeedback
-from ..utils import check_bandit_feedback_inputs
-from ..utils import convert_to_action_dist
+from ..utils import check_bandit_feedback_inputs, check_array
 
 
 # bandit policy type
 BanditPolicy = Union[BaseContextFreePolicy, BaseContextualPolicy]
+coef_func_signature = Callable[
+    [np.ndarray, np.ndarray, np.random.RandomState],
+    Tuple[np.ndarray, np.ndarray, np.ndarray],
+]
 
 
-def run_bandit_simulation(
-    bandit_feedback: BanditFeedback, policy: BanditPolicy
-) -> np.ndarray:
-    """Run an online bandit algorithm on the given logged bandit feedback data.
+@dataclass
+class BanditRound:
+    n_actions: int
+    context: np.ndarray
+    action_context: np.ndarray
+    rewards: np.ndarray
+    expected_rewards: np.ndarray
+    round_delays: np.ndarray
+
+
+@dataclass
+class BanditRounds:
+    n_rounds: int
+    n_actions: int
+    context: np.ndarray
+    action_context: np.ndarray
+    rewards: np.ndarray
+    expected_rewards: np.ndarray
+    round_delays: np.ndarray
+
+    def _get_bandit_round(self) -> BanditRound:
+        if self.round_delays is not None:
+            round_delays = self.round_delays[self.idx]
+        else:
+            round_delays = None
+
+        return BanditRound(
+            n_actions=self.n_actions,
+            context=self.context[self.idx],
+            action_context=self.action_context,
+            rewards=self.rewards[self.idx],
+            expected_rewards=self.expected_rewards[self.idx],
+            round_delays=round_delays,
+        )
+
+    def __iter__(self) -> "BanditRounds":
+        self.idx = 0
+        return self
+
+    def __next__(self) -> BanditRound:
+        if self.idx < len(self):
+            result = self._get_bandit_round()
+            self.idx += 1
+            return result
+        else:
+            raise StopIteration
+
+    def __len__(self) -> int:
+        return self.n_rounds
+
+
+@dataclass
+class BanditEnvironmentSimulator:
+    """Class for simulating an environment that can be used with bandit algorithms.
+
+    Note
+    -----
+    By calling the `obtain_batch_bandit_feedback` method several times,
+    we can resample logged bandit data from the same data generating distribution.
+    This can be used to estimate confidence intervals of the performances of OPE estimators.
+
 
     Parameters
-    ----------
-    bandit_feedback: BanditFeedback
-        Logged bandit data used in offline bandit simulation.
+    -----------
+    n_actions: int
+        Number of actions.
 
-    policy: BanditPolicy
-        Online bandit policy to be evaluated in offline bandit simulation (i.e., evaluation policy).
+    dim_context: int, default=1
+        Number of dimensions of context vectors.
 
-    Returns
-    --------
-    action_dist: array-like, shape (n_rounds, n_actions, len_list)
-        Action choice probabilities (can be deterministic).
+    reward_type: str, default='binary'
+        Type of reward variable, which must be either 'binary' or 'continuous'.
+        When 'binary', rewards are sampled from the Bernoulli distribution.
+        When 'continuous', rewards are sampled from the truncated Normal distribution with `scale=1`.
+        The mean parameter of the reward distribution is determined by the `reward_function` specified by the next argument.
+
+    reward_function: Callable[[np.ndarray, np.ndarray], np.ndarray]], default=None
+        Function defining the expected reward for each given action-context pair,
+        i.e., :math:`q: \\mathcal{X} \\times \\mathcal{A} \\rightarrow \\mathbb{R}`.
+        If None, context **independent** expected rewards will be
+        sampled from the uniform distribution automatically.
+
+    delay_function: Callable[[np.ndarray, np.ndarray], np.ndarray]], default=None
+        Function defining the delay rounds for each given action-context pair,
+        If None, the `delay_rounds` key will be omitted from the dataset samples.
 
+    coef_function: Callable[[np.ndarray, np.ndarray], np.ndarray]], default=sample_random_uniform_coefficients
+        Function responsible for providing coefficients to the reward function. By default coefficients are sampled
+        as random uniform.
+
+    reward_std: float, default=1.0
+        Standard deviation of the reward distribution.
+        A larger value leads to a noisier reward distribution.
+        This argument is valid only when `reward_type="continuous"`.
+
+    action_context: np.ndarray, default=None
+         Vector representation of (discrete) actions.
+         If None, one-hot representation will be used.
+
+    random_state: int, default=12345
+        Controls the random seed in sampling synthetic bandit data.
     """
-    for key_ in ["action", "position", "reward", "pscore", "context"]:
-        if key_ not in bandit_feedback:
-            raise RuntimeError(f"Missing key of {key_} in 'bandit_feedback'.")
-    check_bandit_feedback_inputs(
-        context=bandit_feedback["context"],
-        action=bandit_feedback["action"],
-        reward=bandit_feedback["reward"],
-        position=bandit_feedback["position"],
-        pscore=bandit_feedback["pscore"],
-    )
 
-    policy_ = policy
-    selected_actions_list = list()
-    dim_context = bandit_feedback["context"].shape[1]
-    if bandit_feedback["position"] is None:
-        bandit_feedback["position"] = np.zeros_like(
-            bandit_feedback["action"], dtype=int
-        )
-    for action_, reward_, position_, context_ in tqdm(
-        zip(
-            bandit_feedback["action"],
-            bandit_feedback["reward"],
-            bandit_feedback["position"],
-            bandit_feedback["context"],
-        ),
-        total=bandit_feedback["n_rounds"],
-    ):
-
-        # select a list of actions
-        if policy_.policy_type == PolicyType.CONTEXT_FREE:
-            selected_actions = policy_.select_action()
-        elif policy_.policy_type == PolicyType.CONTEXTUAL:
-            selected_actions = policy_.select_action(context_.reshape(1, dim_context))
-        action_match_ = action_ == selected_actions[position_]
-        # update parameters of a bandit policy
-        # only when selected actions&positions are equal to logged actions&positions
-        if action_match_:
-            if policy_.policy_type == PolicyType.CONTEXT_FREE:
-                policy_.update_params(action=action_, reward=reward_)
-            elif policy_.policy_type == PolicyType.CONTEXTUAL:
-                policy_.update_params(
-                    action=action_,
-                    reward=reward_,
-                    context=context_.reshape(1, dim_context),
+    n_actions: int
+    dim_context: int = 1
+    reward_type: str = RewardType.BINARY.value
+    reward_function: Optional[Callable[[np.ndarray, np.ndarray], np.ndarray]] = None
+    delay_function: Optional[Callable[[int, float], np.ndarray]] = None
+    coef_function: Optional[
+        Callable[[int, float], np.ndarray]
+    ] = sample_random_uniform_coefficients
+    reward_std: float = 1.0
+    action_context: Optional[np.ndarray] = None
+    random_state: int = 12345
+
+    def __post_init__(self) -> None:
+        """Initialize Class."""
+        check_scalar(self.n_actions, "n_actions", int, min_val=2)
+        check_scalar(self.dim_context, "dim_context", int, min_val=1)
+
+        if self.random_state is None:
+            raise ValueError("`random_state` must be given")
+        self.random_ = check_random_state(self.random_state)
+
+        if self.reward_function is None:
+            self.expected_reward = self.sample_contextfree_expected_reward()
+
+        # one-hot encoding characterizing actions.
+        if self.action_context is None:
+            self.action_context = np.eye(self.n_actions, dtype=int)
+        else:
+            check_array(
+                array=self.action_context, name="action_context", expected_dim=2
+            )
+            if self.action_context.shape[0] != self.n_actions:
+                raise ValueError(
+                    "Expected `action_context.shape[0] == n_actions`, but found it False."
                 )
-        selected_actions_list.append(selected_actions)
 
-    action_dist = convert_to_action_dist(
-        n_actions=bandit_feedback["action"].max() + 1,
-        selected_actions=np.array(selected_actions_list),
-    )
-    return action_dist
+    def sample_contextfree_expected_reward(self) -> np.ndarray:
+        """Sample expected reward for each action from the uniform distribution."""
+        return self.random_.uniform(size=self.n_actions)
+
+    def calc_expected_reward(self, context: np.ndarray) -> np.ndarray:
+        """Sample expected rewards given contexts"""
+        # sample reward for each round based on the reward function
+        if self.reward_function is None:
+            expected_reward_ = np.tile(self.expected_reward, (context.shape[0], 1))
+        else:
+            expected_reward_ = self.reward_function(
+                context=context,
+                action_context=self.action_context,
+                random_state=self.random_state,
+                coef_function=self.coef_function,
+            )
+
+        return expected_reward_
+
+    def sample_reward_given_expected_reward(
+        self, expected_reward: np.ndarray
+    ) -> np.ndarray:
+        return self.random_.binomial(n=1, p=expected_reward)
+
+    def sample_reward(self, context: np.ndarray) -> np.ndarray:
+        """Sample rewards given contexts and actions, i.e., :math:`r \\sim p(r | x, a)`.
+
+        Parameters
+        -----------
+        context: array-like, shape (n_rounds, dim_context)
+            Context vectors characterizing each data (such as user information).
+
+        action: array-like, shape (n_rounds,)
+            Actions chosen by the behavior policy for each context.
+
+        Returns
+        ---------
+        reward: array-like, shape (n_rounds,)
+            Sampled rewards given contexts and actions.
+
+        """
+        check_array(array=context, name="context", expected_dim=2)
+        expected_reward_ = self.calc_expected_reward(context)
+        return self.sample_reward_given_expected_reward(expected_reward_)
+
+    def _sample_context(self, size: tuple) -> np.ndarray:
+        context = self.random_.normal(size=size)
+        return context
+
+    def next_context(self) -> np.ndarray:
+        return self._sample_context(size=(1, self.dim_context))
+
+    def next_context_batch(self, n_rounds: int) -> np.ndarray:
+        return self._sample_context(size=(n_rounds, self.dim_context))
+
+    def next_bandit_round(self) -> BanditRound:
+        context = self.next_context()
+        expected_reward_ = self.calc_expected_reward(context)
+        rewards = self.sample_reward_given_expected_reward(expected_reward_)
+
+        round_delays = None
+        if self.delay_function:
+            round_delays = self.delay_function(
+                n_rounds=1,
+                n_actions=self.n_actions,
+                expected_rewards=expected_reward_,
+            )[0]
+
+        return BanditRound(
+            n_actions=self.n_actions,
+            context=context,
+            action_context=self.action_context[0],
+            rewards=rewards[0],
+            expected_rewards=expected_reward_[0],
+            round_delays=round_delays,
+        )
+
+    def next_bandit_round_batch(self, n_rounds: int) -> BanditRounds:
+        """Obtain a batch of full-information bandit data. Contains rewards for all arms,
+
+        Parameters
+        ----------
+        n_rounds: int
+            Data size of the synthetic logged bandit data.
+
+        Returns
+        ---------
+        bandit_feedback: BanditRounds
+            A batch of bandit rounds that can be used to simulate an online bandit model
+
+        """
+        check_scalar(n_rounds, "n_rounds", int, min_val=1)
+
+        contexts = self.next_context_batch(n_rounds=n_rounds)
+        expected_reward_ = self.calc_expected_reward(contexts)
+        rewards = self.sample_reward_given_expected_reward(expected_reward_)
+
+        round_delays = None
+        if self.delay_function:
+            round_delays = self.delay_function(
+                n_rounds=contexts.shape[0],
+                n_actions=self.n_actions,
+                expected_rewards=expected_reward_,
+            )
+
+        return BanditRounds(
+            n_rounds=n_rounds,
+            n_actions=self.n_actions,
+            context=contexts,
+            action_context=self.action_context,
+            rewards=rewards,
+            expected_rewards=expected_reward_,
+            round_delays=round_delays,
+        )
+
+
+@dataclass
+class BanditPolicySimulator:
+    policy: Any
+    environment: BanditEnvironmentSimulator = None
+
+    # Internals
+    reward_round_lookup: defaultdict = None
+
+    # To keep track of for after
+    _selected_actions: List[int] = None
+    _obtained_rewards: List[int] = None
+    _ground_truth_rewards: List[np.ndarray] = None
+    _contexts: List[np.ndarray] = None
+    total_reward: int = 0
+    rounds_played: int = 0
+    current_round: BanditRound = None
+
+    @property
+    def selected_actions(self) -> np.ndarray:
+        return np.asarray(self._selected_actions)
+
+    @property
+    def obtained_rewards(self) -> np.ndarray:
+        return np.asarray(self._obtained_rewards)
+
+    @property
+    def ground_truth_rewards(self) -> np.ndarray:
+        return np.vstack(self._ground_truth_rewards)
+
+    @property
+    def contexts(self) -> np.ndarray:
+        return np.vstack(self._contexts)
+
+    def __post_init__(self):
+        self._selected_actions = []
+        self._obtained_rewards = []
+        self._ground_truth_rewards = []
+        self._contexts = []
+        self.reward_round_lookup = defaultdict(list)
+
+    def start_next_bandit_round(self, bandit_round: BanditRound = None) -> None:
+        if not bandit_round:
+            self.current_round = self.environment.next_bandit_round()
+        else:
+            self.current_round = bandit_round
+
+        self.append_contexts(self.current_round.context)
+        self.append_ground_truth_rewards(self.current_round.rewards)
+
+    def append_ground_truth_rewards(self, ground_truth_rewards):
+        self._ground_truth_rewards.append(ground_truth_rewards)
+
+    def append_contexts(self, context):
+        self._contexts.append(context)
+
+    def step(self, bandit_round: BanditRound = None):
+        self.start_next_bandit_round(bandit_round)
+        self._step()
+
+    def _step(self):
+        selected_action = self.select_action()
+        self._selected_actions.append(selected_action)
+
+        reward_ = self.current_round.rewards[selected_action]
+        self._obtained_rewards.append(reward_)
+        self.total_reward += reward_
+
+        delays = self.current_round.round_delays
+        if delays is None:
+            self.update_policy(self.current_round.context, selected_action, reward_)
+        else:
+            # Add the current round to the lookup
+            round_delay = delays[selected_action]
+            self.reward_round_lookup[round_delay + self.rounds_played].append(
+                self.rounds_played
+            )
+
+            # Update policy with all available rounds
+            available_rounds = self.reward_round_lookup.get(self.rounds_played, [])
+            self.delayed_update_policy(available_rounds, self.rounds_played)
+
+        self.rounds_played += 1
+
+    def select_action(self):
+        if self.policy.policy_type == PolicyType.CONTEXT_FREE:
+            selected_action = self.policy.select_action()[0]
+        elif self.policy.policy_type == PolicyType.CONTEXTUAL:
+            selected_action = self.policy.select_action(
+                np.expand_dims(self.current_round.context, axis=0)
+            )[0]
+        else:
+            raise RuntimeError(
+                f"Policy type {self.policy.policy_type} of policy {self.policy.policy_name} is unsupported"
+            )
+        return selected_action
+
+    def steps(
+        self, n_rounds: int = None, batch_bandit_rounds: BanditRounds = None
+    ) -> None:
+        if n_rounds:
+            for _ in tqdm(range(n_rounds)):
+                self.step()
+        if batch_bandit_rounds:
+            for bandit_round in tqdm(batch_bandit_rounds):
+                self.step(bandit_round)
+
+    def delayed_update_policy(
+        self, available_rounds: List[int], current_round: int
+    ) -> None:
+        for available_round_idx in available_rounds:
+            available_action = self._selected_actions[available_round_idx]
+            available_context = self._contexts[available_round_idx]
+            available_rewards = self._obtained_rewards[available_round_idx]
+
+            self.update_policy(available_context, available_action, available_rewards)
+
+        self.reward_round_lookup.pop(current_round, None)
+
+    def clear_delayed_queue(self):
+        for round_idx, available_rounds in self.reward_round_lookup.copy().items():
+            self.delayed_update_policy(available_rounds, current_round=round_idx)
+
+    def update_policy(self, context: np.ndarray, action: int, reward: int) -> None:
+        """Run an online bandit algorithm on the given logged bandit feedback data.
+
+        Parameters
+        ----------
+        context: np.ndarray
+            Context in which the policy observed the reward
+
+        action: int
+            Action taken by the policy as defined by the `policy` argument
+
+        reward: int
+            Reward observed by the policy as defined by the `policy` argument
+        """
+        if self.policy.policy_type == PolicyType.CONTEXT_FREE:
+            self.policy.update_params(action=action, reward=reward)
+        elif self.policy.policy_type == PolicyType.CONTEXTUAL:
+            self.policy.update_params(
+                action=action,
+                reward=reward,
+                context=np.expand_dims(context, axis=0),
+            )
 
 
 def calc_ground_truth_policy_value(
diff --git a/obp/utils.py b/obp/utils.py
index a217d3bd..d3e97807 100755
--- a/obp/utils.py
+++ b/obp/utils.py
@@ -118,7 +118,7 @@ def convert_to_action_dist(
     n_actions: int,
     selected_actions: np.ndarray,
 ) -> np.ndarray:
-    """Convert selected actions (output of `run_bandit_simulation`) to distribution over actions.
+    """Convert selected actions (output of `run_bandit_replay`) to distribution over actions.
 
     Parameters
     ----------
diff --git a/tests/dataset/test_synthetic.py b/tests/dataset/test_synthetic.py
index beea3a6a..8313676c 100644
--- a/tests/dataset/test_synthetic.py
+++ b/tests/dataset/test_synthetic.py
@@ -2,7 +2,10 @@
 import pytest
 
 from obp.dataset import SyntheticBanditDataset
-from obp.dataset.synthetic import linear_behavior_policy
+from obp.dataset.synthetic import (
+    linear_behavior_policy,
+    _base_reward_function,
+)
 from obp.dataset.synthetic import linear_reward_function
 from obp.dataset.synthetic import logistic_polynomial_reward_function
 from obp.dataset.synthetic import logistic_reward_function
@@ -557,3 +560,26 @@ def test_synthetic_behavior_policy_function():
         )
         assert action_prob.shape[0] == n_rounds and action_prob.shape[1] == n_actions
         assert np.all(0 <= action_prob) and np.all(action_prob <= 1)
+
+
+def test_base_reward_create_a_matrix_with_expected_rewards_with_identical_expectation_for_identical_rounds_():
+    context = np.asarray(
+        [
+            [1, 2],
+            [3, 2],
+            [3, 2],
+        ]
+    )
+    action_context = np.asarray(
+        [
+            [1, 0, 0],
+            [0, 0, 1],
+            [0, 1, 0],
+        ]
+    )
+    actual_expected_rewards = _base_reward_function(
+        context, action_context, degree=5, effective_dim_ratio=1.0, random_state=12345
+    )
+
+    assert np.allclose(actual_expected_rewards[1], actual_expected_rewards[2])
+    assert not np.allclose(actual_expected_rewards[0], actual_expected_rewards[1])
diff --git a/tests/ope/conftest.py b/tests/ope/conftest.py
index 939105eb..8ad6e1c7 100644
--- a/tests/ope/conftest.py
+++ b/tests/ope/conftest.py
@@ -158,25 +158,6 @@ def feedback_key_set() -> Set[str]:
     }
 
 
-# bandit_feedback["expected_reward"][0]
-@pytest.fixture(scope="session")
-def expected_reward_0() -> np.ndarray:
-    return np.array(
-        [
-            0.816124,
-            0.625855,
-            0.386785,
-            0.301848,
-            0.726634,
-            0.218076,
-            0.482361,
-            0.625271,
-            0.586353,
-            0.386384,
-        ]
-    )
-
-
 # random evaluation policy
 @pytest.fixture(scope="session")
 def random_action_dist(synthetic_bandit_feedback) -> np.ndarray:
diff --git a/tests/ope/test_all_estimators.py b/tests/ope/test_all_estimators.py
index c9b99cf3..a3e5b94c 100644
--- a/tests/ope/test_all_estimators.py
+++ b/tests/ope/test_all_estimators.py
@@ -1,5 +1,3 @@
-from typing import Set
-
 from conftest import generate_action_dist
 import numpy as np
 import pytest
@@ -528,23 +526,6 @@ def test_estimate_intervals_of_all_estimators_using_valid_input_data(
         )
 
 
-def test_fixture(
-    synthetic_bandit_feedback: BanditFeedback,
-    expected_reward_0: np.ndarray,
-    feedback_key_set: Set[str],
-    random_action_dist: np.ndarray,
-) -> None:
-    """
-    Check the validity of the fixture data generated by conftest.py
-    """
-    np.testing.assert_array_almost_equal(
-        expected_reward_0, synthetic_bandit_feedback["expected_reward"][0]
-    )
-    assert feedback_key_set == set(
-        synthetic_bandit_feedback.keys()
-    ), f"Key set of bandit feedback should be {feedback_key_set}, but {synthetic_bandit_feedback.keys()}"
-
-
 def test_performance_of_ope_estimators_using_random_evaluation_policy(
     synthetic_bandit_feedback: BanditFeedback, random_action_dist: np.ndarray
 ) -> None:
diff --git a/tests/ope/test_offline_estimation_performance.py b/tests/ope/test_offline_estimation_performance.py
index a9e76cf6..2079bfdf 100644
--- a/tests/ope/test_offline_estimation_performance.py
+++ b/tests/ope/test_offline_estimation_performance.py
@@ -345,7 +345,7 @@ def process(i: int):
 
         return relative_ee_i
 
-    n_runs = 10
+    n_runs = 20
     processed = Parallel(
         n_jobs=-1,
         verbose=0,
diff --git a/tests/simulator/test_coefficient_drifter.py b/tests/simulator/test_coefficient_drifter.py
new file mode 100644
index 00000000..73bfa2f5
--- /dev/null
+++ b/tests/simulator/test_coefficient_drifter.py
@@ -0,0 +1,424 @@
+from unittest import mock
+
+import numpy as np
+import pytest
+
+from obp.dataset.synthetic import logistic_sparse_reward_function, _base_reward_function
+from obp.simulator.coefficient_drifter import CoefficientDrifter
+from obp.simulator.simulator import BanditEnvironmentSimulator
+
+
+def test_coefficient_tracker_can_shift_expected_rewards_with_syntethic_dataset_generator():
+    drifter = CoefficientDrifter(drift_interval=3)
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=4,
+        reward_type="binary",  # "binary" or "continuous"
+        reward_function=logistic_sparse_reward_function,
+        coef_function=drifter.get_coefficients,
+        random_state=12345,
+    )
+
+    _ = dataset.next_bandit_round_batch(n_rounds=4)
+
+
+class MockCoefSample:
+    n_samples = 0
+
+    def fake_sample(
+        self, effective_dim_action_context, effective_dim_context, random_, **kwargs
+    ):
+        self.n_samples += 1
+        context_coef_ = self.n_samples * np.ones(effective_dim_context)
+        action_coef_ = self.n_samples * np.ones(effective_dim_action_context)
+        context_action_coef_ = self.n_samples * np.ones(
+            (effective_dim_context, effective_dim_action_context)
+        )
+
+        return context_coef_, action_coef_, context_action_coef_
+
+
+def test_coefficient_tracker_can_shift_expected_rewards_instantly_based_on_configured_intervals():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(drift_interval=3)
+
+        context = np.asarray(
+            [
+                [1, 2],
+                [3, 2],
+                [3, 2],
+                [3, 2],
+            ]
+        )
+        action_context = np.asarray(
+            [
+                [1, 0, 0],
+                [0, 0, 1],
+                [0, 1, 0],
+            ]
+        )
+        actual_expected_rewards = _base_reward_function(
+            context,
+            action_context,
+            degree=5,
+            effective_dim_ratio=1.0,
+            coef_function=drifter.get_coefficients,
+            random_state=12345,
+        )
+
+    # This round has a different context and should have diff E[r]
+    assert not np.allclose(actual_expected_rewards[0], actual_expected_rewards[1])
+    # The next two rounds have the same context and should have identical
+    assert np.allclose(actual_expected_rewards[1], actual_expected_rewards[2])
+    # This round has the same context but has experienced drift.
+    assert not np.allclose(actual_expected_rewards[2], actual_expected_rewards[3])
+
+
+def test_coefficient_tracker_can_shift_coefficient_instantly_based_on_configured_interval():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        effective_dim_context = 4
+        effective_dim_action_context = 3
+        drifter = CoefficientDrifter(
+            drift_interval=3,
+            effective_dim_context=effective_dim_context,
+            effective_dim_action_context=effective_dim_action_context,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=4)
+
+    expected_context_coef = np.asarray(
+        [
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],
+            [1, 1, 1, 1],  # AFTER THIS ROUND, THE COEFS CHANGE ABRUPTLY
+            [2, 2, 2, 2],
+        ]
+    )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_can_shift_linearly_instantly_based_on_configured_transition_period():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=4,
+            transition_period=2,
+            effective_dim_context=2,
+            effective_dim_action_context=2,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=8)
+
+    expected_context_coef = np.asarray(
+        [
+            [1.0, 1.0],
+            [1.0, 1.0],  # First two rounds are the same
+            [1.33333333, 1.33333333],  # Next two rounds slowly transition
+            [1.66666667, 1.66666667],
+            [2.0, 2.0],  # Now we start in the new coef again
+            [2.0, 2.0],
+            [2.33333333, 2.33333333],  # Now we start transitioning again.
+            [2.66666667, 2.66666667],
+        ]
+    )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_can_shift_weighted_sampled_based_on_configured_transition_period():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=4,
+            transition_period=2,
+            transition_type="weighted_sampled",
+            effective_dim_context=2,
+            effective_dim_action_context=2,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=8)
+
+    expected_context_coef = np.asarray(
+        [
+            [1.0, 1.0],
+            [1.0, 1.0],  # First two rounds are the same
+            [1.0, 1.0],  # Next two rounds are weighted sampled
+            [2.0, 2.0],
+            [2.0, 2.0],  # Now we start in the new coef again
+            [2.0, 2.0],
+            [3.0, 3.0],  # Next two rounds are weighted sampled again
+            [3.0, 3.0],
+        ]
+    )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_can_shift_instantly_back_and_forth_between_seasons_using_seasonality_flag():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            transition_period=0,
+            seasonal=True,
+            effective_dim_context=2,
+            effective_dim_action_context=2,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=8)
+
+    expected_context_coef = np.asarray(
+        [
+            [1.0, 1.0],
+            [1.0, 1.0],
+            [2.0, 2.0],
+            [2.0, 2.0],
+            [1.0, 1.0],
+            [1.0, 1.0],
+            [2.0, 2.0],
+            [2.0, 2.0],
+        ]
+    )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_can_shift_instantly_under_base_coeficient():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            transition_period=0,
+            seasonal=True,
+            base_coefficient_weight=0.8,
+            effective_dim_context=2,
+            effective_dim_action_context=2,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=8)
+
+        expected_context_coef = np.asarray(
+            [
+                [1, 1],
+                [1, 1],
+                [1.2, 1.2],
+                [1.2, 1.2],
+                [1, 1],
+                [1, 1],
+                [1.2, 1.2],
+                [1.2, 1.2],
+            ]
+        )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_update_coef_makes_next_coef_current_coef_and_samples_new_next_coef():
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=4, effective_dim_context=1, effective_dim_action_context=1
+        )
+
+        drifter.context_coefs[0] = [1]
+        drifter.context_coefs[1] = [2]
+
+        drifter.update_coef()
+
+        assert drifter.context_coefs[0] == [2]
+        assert np.allclose(drifter.context_coefs[1], [3.0])
+
+
+def test_coefficient_tracker_update_coef_samples_both_new_curr_and_next_on_first_pull():
+    drifter = CoefficientDrifter(
+        drift_interval=4,
+    )
+
+    assert len(drifter.context_coefs) == 0
+
+    drifter.effective_dim_context = 1
+    drifter.effective_dim_action_context = 1
+
+    drifter.update_coef()
+
+    assert drifter.context_coefs[0] is not None
+    assert drifter.context_coefs[1] is not None
+    assert not np.allclose(drifter.context_coefs[0], drifter.context_coefs[1])
+
+
+def test_coefficient_tracker_can_set_effective_dim_context_on_first_sample():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+    drifter = CoefficientDrifter(drift_interval=3)
+
+    assert drifter.effective_dim_context is None
+    assert drifter.effective_dim_action_context is None
+
+    actual_context_coef, _, _ = drifter.get_coefficients(
+        n_rounds=4,
+        effective_dim_context=effective_dim_context,
+        effective_dim_action_context=effective_dim_action_context,
+    )
+
+    assert drifter.effective_dim_context == 4
+    assert drifter.effective_dim_action_context == 3
+
+
+def test_coefficient_tracker_raises_when_effective_dimensions_are_being_changed():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+    drifter = CoefficientDrifter(
+        drift_interval=3,
+        effective_dim_context=effective_dim_context,
+        effective_dim_action_context=effective_dim_action_context,
+    )
+
+    assert drifter.effective_dim_context == 4
+    assert drifter.effective_dim_action_context == 3
+
+    with pytest.raises(
+        RuntimeError, match=r"Trying to change the effective dimensions"
+    ):
+        actual_context_coef, _, _ = drifter.get_coefficients(
+            n_rounds=4, effective_dim_context=5, effective_dim_action_context=6
+        )
+
+
+def test_coefficient_tracker_can_shift_coefficient_multiple_times_instantly_based_on_configured_interval():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            effective_dim_context=effective_dim_context,
+            effective_dim_action_context=effective_dim_action_context,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=5)
+
+    expected_context_coef = np.asarray(
+        [
+            [1.0, 1.0, 1.0, 1.0],
+            [1.0, 1.0, 1.0, 1.0],  # AFTER THIS ROUND, THE COEFS SHOULD CHANGE
+            [2.0, 2.0, 2.0, 2.0],
+            [2.0, 2.0, 2.0, 2.0],  # AFTER THIS ROUND, THE COEFS SHOULD CHANGE AGAIN
+            [3.0, 3.0, 3.0, 3.0],
+        ]
+    )
+
+    assert np.allclose(actual_context_coef, expected_context_coef)
+
+
+def test_coefficient_tracker_keeps_track_of_shifted_coefficient_based_on_configured_interval_between_batches():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            effective_dim_context=effective_dim_context,
+            effective_dim_action_context=effective_dim_action_context,
+        )
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=3)
+
+        expected_context_coef = np.asarray(
+            [
+                [1.0, 1.0, 1.0, 1.0],
+                [1.0, 1.0, 1.0, 1.0],  # AFTER THIS ROUND, THE COEFS SHOULD CHANGE
+                [2.0, 2.0, 2.0, 2.0],
+            ]
+        )
+
+        assert np.allclose(actual_context_coef, expected_context_coef)
+
+        actual_context_coef, _, _ = drifter.get_coefficients(n_rounds=3)
+
+        expected_context_coef_2 = np.asarray(
+            [
+                [2.0, 2.0, 2.0, 2.0],  # THIS ROUND SHOULD BE THE SAME AS THE LAST ONE
+                [3.0, 3.0, 3.0, 3.0],  # HERE THE COEF SHOULD CHANGE AGAIN
+                [3.0, 3.0, 3.0, 3.0],
+            ]
+        )
+
+    assert np.allclose(actual_context_coef, expected_context_coef_2)
+
+
+def test_coefficients_can_drift_for_the_action_coefs():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            effective_dim_context=effective_dim_context,
+            effective_dim_action_context=effective_dim_action_context,
+        )
+
+        _, actual_action_coef, _ = drifter.get_coefficients(n_rounds=3)
+
+        expected_action_coef = np.asarray(
+            [
+                [1.0, 1.0, 1.0],
+                [1.0, 1.0, 1.0],  # AFTER THIS ROUND, THE COEFS SHOULD CHANGE
+                [2.0, 2.0, 2.0],
+            ]
+        )
+
+        assert np.allclose(actual_action_coef, expected_action_coef)
+
+
+def test_coefficients_can_drift_for_the_action_context_coefs():
+    effective_dim_context = 4
+    effective_dim_action_context = 3
+
+    with mock.patch(
+        "obp.simulator.coefficient_drifter.sample_random_uniform_coefficients",
+        MockCoefSample().fake_sample,
+    ):
+        drifter = CoefficientDrifter(
+            drift_interval=2,
+            effective_dim_context=effective_dim_context,
+            effective_dim_action_context=effective_dim_action_context,
+        )
+
+        _, _, actual_context_action_coef = drifter.get_coefficients(n_rounds=3)
+
+        expected_context_action_coef = np.asarray(
+            [
+                [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+                [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+                [[2.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0]],
+            ]
+        )
+
+        assert np.allclose(actual_context_action_coef, expected_context_action_coef)
diff --git a/tests/simulator/test_delay_sampler.py b/tests/simulator/test_delay_sampler.py
new file mode 100644
index 00000000..744a8cea
--- /dev/null
+++ b/tests/simulator/test_delay_sampler.py
@@ -0,0 +1,193 @@
+import numpy as np
+import pytest
+from obp.dataset.synthetic import logistic_sparse_reward_function
+
+from obp.simulator.delay_sampler import ExponentialDelaySampler
+from obp.simulator.simulator import BanditEnvironmentSimulator
+
+
+def test_synthetic_sample_results_in_sampled_delay_when_delay_function_is_given():
+    n_actions = 3
+    delay_function = ExponentialDelaySampler(
+        max_scale=100.0, random_state=12345
+    ).exponential_delay_function
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+
+    actual_bandits_dataset = dataset.next_bandit_round_batch(n_rounds=5)
+
+    expected_round_delays = np.tile([266.0, 39.0, 21.0, 23.0, 84.0], (n_actions, 1)).T
+    assert (actual_bandits_dataset.round_delays == expected_round_delays).all()
+
+
+def test_synthetic_sample_results_in_sampled_delay_with_weighted_delays_per_arm():
+    n_actions = 3
+    delay_function = ExponentialDelaySampler(
+        max_scale=100.0, min_scale=10.0, random_state=12345
+    ).exponential_delay_function_expected_reward_weighted
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+
+    actual_bandits_dataset = dataset.next_bandit_round_batch(n_rounds=1000)
+
+    ordered_rewards = actual_bandits_dataset.expected_rewards[0].argsort()
+    mean_delays = actual_bandits_dataset.round_delays.sum(axis=0)
+    assert (
+        mean_delays[ordered_rewards[2]]
+        < mean_delays[ordered_rewards[1]]
+        > mean_delays[ordered_rewards[2]]
+    )
+
+
+def test_synthetic_sample_results_with_exponential_delay_function_has_different_delays_each_batch():
+    n_actions = 3
+
+    delay_function = ExponentialDelaySampler(
+        max_scale=1000.0, random_state=12345
+    ).exponential_delay_function
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+
+    actual_delays_1 = dataset.next_bandit_round_batch(n_rounds=5).round_delays
+    actual_delays_2 = dataset.next_bandit_round_batch(n_rounds=5).round_delays
+
+    expected_round_delays_1 = np.tile(
+        [2654.0, 381.0, 204.0, 229.0, 839.0], (n_actions, 1)
+    ).T
+    expected_round_delays_2 = np.tile(
+        [906.0, 3339.0, 1059.0, 1382.0, 1061.0], (n_actions, 1)
+    ).T
+
+    assert (actual_delays_1 == expected_round_delays_1).all()
+    assert (actual_delays_2 == expected_round_delays_2).all()
+
+
+def test_synthetic_sample_results_with_exponential_delay_function_has_same_delays_each_dataset():
+    n_actions = 3
+
+    delay_function = ExponentialDelaySampler(
+        max_scale=1000.0, random_state=12345
+    ).exponential_delay_function
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+
+    actual_delays_1 = dataset.next_bandit_round_batch(n_rounds=5).round_delays
+
+    delay_function = ExponentialDelaySampler(
+        max_scale=1000.0, random_state=12345
+    ).exponential_delay_function
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+    actual_delays_2 = dataset.next_bandit_round_batch(n_rounds=5).round_delays
+
+    expected_round_delays_1 = np.tile(
+        [2654.0, 381.0, 204.0, 229.0, 839.0], (n_actions, 1)
+    ).T
+    expected_round_delays_2 = np.tile(
+        [2654.0, 381.0, 204.0, 229.0, 839.0], (n_actions, 1)
+    ).T
+
+    assert (actual_delays_1 == expected_round_delays_1).all()
+    assert (actual_delays_2 == expected_round_delays_2).all()
+
+
+def test_synthetic_sample_results_do_not_contain_reward_delay_when_delay_function_is_none():
+    dataset = BanditEnvironmentSimulator(
+        n_actions=3,
+        reward_function=logistic_sparse_reward_function,
+        random_state=12345,
+    )
+
+    actual_bandits_dataset = dataset.next_bandit_round_batch(n_rounds=5)
+
+    assert actual_bandits_dataset.round_delays is None
+
+
+def test_synthetic_sample_results_reward_delay_is_configurable_through_delay_function():
+    def trivial_delay_func(*args, **kwargs):
+        return np.asarray([1, 2, 3, 4, 5])
+
+    dataset = BanditEnvironmentSimulator(
+        n_actions=3,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=trivial_delay_func,
+        random_state=12345,
+    )
+
+    actual_bandits_dataset = dataset.next_bandit_round_batch(n_rounds=5)
+
+    assert (actual_bandits_dataset.round_delays == [1, 2, 3, 4, 5]).all()
+
+
+@pytest.mark.parametrize(
+    "size, actions, random_state, expected_delays",
+    [
+        (5, 3, 12345, np.tile([266.0, 39.0, 21.0, 23.0, 84.0], (3, 1)).T),
+        (3, 3, 12345, np.tile([266.0, 39.0, 21.0], (3, 1)).T),
+        (5, 3, 54321, np.tile([243.0, 98.0, 157.0, 57.0, 79.0], (3, 1)).T),
+    ],
+)
+def test_exponential_delay_function_results_in_expected_seeded_discrete_delays(
+    size, actions, random_state, expected_delays
+):
+    delay_function = ExponentialDelaySampler(
+        max_scale=100.0, random_state=random_state
+    ).exponential_delay_function
+
+    actual_delays = delay_function(size, n_actions=actions)
+    assert (actual_delays == expected_delays).all()
+
+
+@pytest.mark.parametrize(
+    "size, actions, expected_reward, random_state, expected_delays",
+    [
+        (
+            2,
+            2,
+            np.asarray([[1, 0.01], [0.5, 0.5]]),
+            12344,
+            np.asarray([[2.0, 55.0], [3.0, 27.0]]),
+        ),
+        (
+            2,
+            2,
+            np.asarray([[0.1, 0.2], [0.3, 0.4]]),
+            12345,
+            np.asarray([[242.0, 32.0], [15.0, 15.0]]),
+        ),
+    ],
+)
+def test_exponential_delay_function_conditioned_on_expected_reward_results_in_expected_seeded_discrete_delays(
+    size, actions, expected_reward, random_state, expected_delays
+):
+    delay_function = ExponentialDelaySampler(
+        max_scale=100.0, min_scale=10.0, random_state=random_state
+    ).exponential_delay_function_expected_reward_weighted
+
+    actual_delays = delay_function(expected_rewards=expected_reward)
+    assert (actual_delays == expected_delays).all()
diff --git a/tests/simulator/test_simulator.py b/tests/simulator/test_simulator.py
new file mode 100644
index 00000000..813a2f22
--- /dev/null
+++ b/tests/simulator/test_simulator.py
@@ -0,0 +1,576 @@
+import re
+
+import numpy as np
+import pytest
+from sklearn.linear_model import LogisticRegression
+
+from obp.policy import BaseContextFreePolicy, IPWLearner
+from obp.policy.linear import LinTS, LinUCB
+
+from obp.policy.contextfree import EpsilonGreedy, Random, BernoulliTS
+from obp.dataset.synthetic import (
+    logistic_reward_function,
+    logistic_sparse_reward_function,
+)
+from obp.policy.policy_type import PolicyType
+from obp.simulator.coefficient_drifter import CoefficientDrifter
+from obp.simulator.delay_sampler import ExponentialDelaySampler
+from obp.simulator.simulator import BanditEnvironmentSimulator, BanditPolicySimulator
+
+
+def test_bandit_policy_simulator_updates_at_each_taken_action():
+    n_rounds = 100
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=5,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+
+    epsilon_greedy = EpsilonGreedy(n_actions=3)
+
+    simulator = BanditPolicySimulator(
+        policy=epsilon_greedy,
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+    assert epsilon_greedy.n_trial == n_rounds
+
+
+def test_bandit_policy_simulator_handles_context_in_simulations():
+    n_rounds = 100
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=5,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+
+    lin_ts = LinTS(dim=env.dim_context, n_actions=env.n_actions, random_state=12345)
+    simulator = BanditPolicySimulator(
+        policy=lin_ts,
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+    assert lin_ts.n_trial == n_rounds
+
+
+def test_bandit_policy_simulator_raises_on_unknown_policy():
+    n_rounds = 1
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+
+    class OfflineEpsilon(EpsilonGreedy):
+        @property
+        def policy_type(self) -> PolicyType:
+            return PolicyType.OFFLINE
+
+    epsilon_greedy = OfflineEpsilon(n_actions=3)
+    simulator = BanditPolicySimulator(
+        policy=epsilon_greedy,
+        environment=env,
+    )
+
+    with pytest.raises(
+        RuntimeError,
+        match=re.escape(
+            r"Policy type PolicyType.OFFLINE of policy egreedy_1.0 is " r"unsupported"
+        ),
+    ):
+        simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+
+class BanditUpdateTracker(BaseContextFreePolicy):
+    """
+    This class can be used to keep track on updates being sent to the policy.
+    The policy itself always provides a random choice and should not be used for any analytical purposes.
+    """
+
+    n_rounds = 0
+
+    def __post_init__(self) -> None:
+        self.parameter_updates = []
+        super().__post_init__()
+
+    def select_action(self) -> np.ndarray:
+        self.n_rounds += 1
+        return self.random_.choice(self.n_actions, size=self.len_list, replace=False)
+
+    def update_params(self, action: int, reward: float) -> None:
+        self.parameter_updates.append(
+            {"round": self.n_rounds, "action": action, "reward": reward}
+        )
+
+
+def test_bandit_policy_simulator_works_end_to_end_with_synthetic_bandit_dataset():
+    delay_function = ExponentialDelaySampler(
+        max_scale=1.0, random_state=12345
+    ).exponential_delay_function
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        delay_function=delay_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=5)
+
+    policy = EpsilonGreedy(n_actions=3, epsilon=0.1, random_state=12345)
+
+    simulator = BanditPolicySimulator(
+        policy=policy,
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+
+def test_bandit_policy_simulator_applies_policy_in_delay_specified_order():
+    n_rounds = 5
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+    bandit_rounds.round_delays = np.tile([2, 1, 2, 1, 0], (3, 1)).T
+
+    tracker = BanditUpdateTracker(n_actions=3, random_state=12345)
+
+    simulator = BanditPolicySimulator(
+        policy=tracker,
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+    expected_updates = [
+        {"round": 3, "action": 0, "reward": 1},
+        {"round": 3, "action": 0, "reward": 1},
+        {"round": 5, "action": 2, "reward": 1},
+        {"round": 5, "action": 1, "reward": 0},
+        {"round": 5, "action": 2, "reward": 0},
+    ]
+
+    assert tracker.parameter_updates == expected_updates
+
+
+@pytest.mark.parametrize(
+    "policy",
+    [
+        Random(n_actions=3, epsilon=1.0, random_state=12345),
+        EpsilonGreedy(n_actions=3, epsilon=0.1, random_state=12345),
+        BernoulliTS(n_actions=3, random_state=12345),
+        LinTS(dim=4, n_actions=3, random_state=12345),
+        LinUCB(dim=4, n_actions=3, random_state=12345),
+    ],
+)
+def test_bandit_policy_simulator_does_not_crash_with_various_bandit_algorithms(policy):
+    n_rounds = 5
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=4,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+    bandit_rounds.round_delays = np.tile([2, 1, 2, 2, 2], (3, 1)).T
+
+    simulator = BanditPolicySimulator(
+        policy=policy,
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+
+def test_bandit_policy_simulator_applies_policy_directly_when_no_delay():
+    n_rounds = 5
+
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+    bandit_rounds = env.next_bandit_round_batch(n_rounds=n_rounds)
+    bandit_rounds.round_delays = None
+
+    tracker = BanditUpdateTracker(n_actions=3, random_state=12345)
+
+    simulator = BanditPolicySimulator(
+        policy=tracker,
+        environment=env,
+    )
+    simulator.steps(batch_bandit_rounds=bandit_rounds)
+
+    expected_updates = [
+        {"action": 0, "reward": 1, "round": 1},
+        {"action": 0, "reward": 1, "round": 2},
+        {"action": 2, "reward": 1, "round": 3},
+        {"action": 1, "reward": 0, "round": 4},
+        {"action": 2, "reward": 0, "round": 5},
+    ]
+
+    assert tracker.parameter_updates == expected_updates
+
+
+def test_simulator_can_create_identical_simulations_using_seeds():
+    sample_n_rounds = 40
+    drift_interval = 20
+    transition_period = 0
+    n_actions = 3
+    dim_context = 5
+
+    drifter = CoefficientDrifter(
+        drift_interval=drift_interval,
+        transition_period=transition_period,
+        transition_type="linear",  # linear or weighted_sampled
+        seasonal=False,
+        base_coefficient_weight=0.3,
+        random_state=1234,
+    )
+
+    env_1 = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        dim_context=dim_context,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        coef_function=drifter.get_coefficients,
+        random_state=12345,
+    )
+
+    policy = EpsilonGreedy(n_actions=n_actions, epsilon=0.1, random_state=12345)
+    simulator_1 = BanditPolicySimulator(
+        policy=policy,
+        environment=env_1,
+    )
+    simulator_1.steps(n_rounds=sample_n_rounds)
+
+    drifter = CoefficientDrifter(
+        drift_interval=drift_interval,
+        transition_period=transition_period,
+        transition_type="linear",  # linear or weighted_sampled
+        seasonal=False,
+        base_coefficient_weight=0.3,
+        random_state=1234,
+    )
+
+    env_2 = BanditEnvironmentSimulator(
+        n_actions=n_actions,
+        dim_context=dim_context,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        coef_function=drifter.get_coefficients,
+        random_state=12345,
+    )
+
+    policy = EpsilonGreedy(n_actions=n_actions, epsilon=0.1, random_state=12345)
+    simulator_2 = BanditPolicySimulator(
+        policy=policy,
+        environment=env_2,
+    )
+    simulator_2.steps(n_rounds=sample_n_rounds)
+
+    assert simulator_1.total_reward == simulator_2.total_reward
+
+
+def test_bandit_environment_simulator_fetches_new_context_every_pull():
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=6,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        random_state=12345,
+    )
+
+    contexts = [env.next_context() for _ in range(5)]
+    assert len(np.hstack(contexts)[0]) == len(np.unique(contexts))
+
+
+def test_bandit_environment_simulator_can_fetch_a_batch_of_contexts():
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=6,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        random_state=12345,
+    )
+
+    contexts = env.next_context_batch(n_rounds=5)
+    assert contexts.shape == (5, 6)
+
+
+def test_bandit_environment_simulator_can_a_single_step():
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=6,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        random_state=12345,
+    )
+
+    _ = env.next_bandit_round()
+
+
+def test_bandit_policy_simulator_can_a_single_steps_and_keep_track():
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=6,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        random_state=12345,
+    )
+
+    simulator = BanditPolicySimulator(
+        policy=EpsilonGreedy(epsilon=0.1, random_state=12345, n_actions=10),
+        environment=env,
+    )
+
+    for i in range(100):
+        assert simulator.rounds_played == i
+        assert len(simulator.selected_actions) == i
+        simulator.step()
+
+    assert simulator.total_reward > 1
+
+
+def test_bandit_policy_simulator_can_do_multiple_steps_in_call_and_keep_track_of_actions_and_performance():
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=6,
+        reward_function=logistic_sparse_reward_function,
+        delay_function=None,
+        random_state=12345,
+    )
+
+    simulator = BanditPolicySimulator(
+        policy=EpsilonGreedy(epsilon=0.1, random_state=12345, n_actions=10),
+        environment=env,
+    )
+
+    simulator.steps(n_rounds=100)
+
+    assert simulator.total_reward > 1
+    assert simulator.rounds_played == 100
+    assert len(simulator.selected_actions) == 100
+    assert len(simulator.obtained_rewards) == 100
+
+
+def test_bandit_policy_simulator_can_update_policy_with_delays_if_delay_rounds_are_available():
+    class MockBanditEnvironmentSimulator(BanditEnvironmentSimulator):
+        round_delays = np.tile([1, 1, 2, 1, 0], (5, 1)).T
+
+        def next_bandit_round(self):
+            bandit_round = super().next_bandit_round()
+            bandit_round.round_delays, self.round_delays = (
+                self.round_delays[-1],
+                self.round_delays[:-1],
+            )
+            return bandit_round
+
+    env = MockBanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    tracker = BanditUpdateTracker(n_actions=3, random_state=12345)
+
+    simulator = BanditPolicySimulator(
+        policy=tracker,
+        environment=env,
+    )
+
+    simulator.steps(n_rounds=5)
+
+    expected_updates = [1, 3, 5, 5]
+
+    assert [update["round"] for update in tracker.parameter_updates] == expected_updates
+
+
+def test_bandit_policy_simulator_clears_delay_queue_when_called_into_last_available_round():
+    class MockBanditEnvironmentSimulator(BanditEnvironmentSimulator):
+        round_delays = np.tile([1, 1, 4, 3, 2], (5, 1)).T
+
+        def next_bandit_round(self):
+            bandit_round = super().next_bandit_round()
+            bandit_round.round_delays, self.round_delays = (
+                self.round_delays[-1],
+                self.round_delays[:-1],
+            )
+            return bandit_round
+
+    env = MockBanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    tracker = BanditUpdateTracker(n_actions=3, random_state=12345)
+
+    simulator = BanditPolicySimulator(
+        policy=tracker,
+        environment=env,
+    )
+
+    simulator.steps(n_rounds=5)
+
+    expected_updates_before_queue_cleared = [3, 5, 5]
+    assert [
+        update["round"] for update in tracker.parameter_updates
+    ] == expected_updates_before_queue_cleared
+
+    simulator.clear_delayed_queue()
+
+    expected_updates_before_queue_cleared = [3, 5, 5, 5, 5]
+
+    assert len(simulator.reward_round_lookup.values()) == 0
+    assert [
+        update["round"] for update in tracker.parameter_updates
+    ] == expected_updates_before_queue_cleared
+
+
+def test_bandit_policy_simulator_do_simulation_over_batch_data():
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=1,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    simulator = BanditPolicySimulator(
+        policy=EpsilonGreedy(n_actions=3, epsilon=0.1, random_state=12345),
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=env.next_bandit_round_batch(5))
+
+    assert simulator.rounds_played == 5
+
+
+def test_bandit_policy_simulator_cleans_up_when_simulation_is_interupted():
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=4,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    class CrashingBanditPolicySimulator(BanditPolicySimulator):
+        def _step(self):
+            super()._step()
+            if self.rounds_played == 50:
+                raise RuntimeError
+
+    simulator = CrashingBanditPolicySimulator(
+        policy=EpsilonGreedy(n_actions=3, epsilon=0.1, random_state=12345),
+        environment=env,
+    )
+
+    with pytest.raises(RuntimeError):
+        simulator.steps(batch_bandit_rounds=env.next_bandit_round_batch(100))
+
+    assert simulator.rounds_played == 50
+    assert simulator.contexts.shape == (50, 4)
+    assert simulator.ground_truth_rewards.shape == (50, 3)
+
+
+def test_bandit_policy_simulator_cleans_up_keeping_previous_rounds_when_simulation_is_interupted():
+    env = BanditEnvironmentSimulator(
+        n_actions=3,
+        dim_context=4,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    class CrashingBanditPolicySimulator(BanditPolicySimulator):
+        def _step(self):
+            super()._step()
+            if self.rounds_played == 5:
+                raise RuntimeError
+
+    simulator = CrashingBanditPolicySimulator(
+        policy=EpsilonGreedy(n_actions=3, epsilon=0.1, random_state=12345),
+        environment=env,
+    )
+
+    batch_1 = env.next_bandit_round_batch(2)
+    simulator.steps(batch_bandit_rounds=batch_1)
+
+    batch_2 = env.next_bandit_round_batch(6)
+    with pytest.raises(RuntimeError):
+        simulator.steps(batch_bandit_rounds=batch_2)
+
+    assert simulator.rounds_played == 5
+    assert np.allclose(
+        simulator.contexts,
+        np.vstack((batch_1.context, batch_2.context))[
+            :5,
+        ],
+    )
+    assert np.allclose(
+        simulator.ground_truth_rewards,
+        np.vstack((batch_1.rewards, batch_2.rewards))[
+            :5,
+        ],
+    )
+
+
+def test_ipw_can_be_learned_from_logged_data_generated_by_simulation():
+    from sklearn.ensemble import RandomForestClassifier as RandomForest
+
+    env = BanditEnvironmentSimulator(
+        n_actions=10,
+        dim_context=5,
+        reward_function=logistic_reward_function,
+        random_state=12345,
+    )
+
+    simulator = BanditPolicySimulator(
+        policy=EpsilonGreedy(n_actions=10, epsilon=0.1, random_state=12345),
+        environment=env,
+    )
+
+    simulator.steps(batch_bandit_rounds=env.next_bandit_round_batch(100))
+
+    propensity_model = LogisticRegression(random_state=12345)
+    propensity_model.fit(simulator.contexts, simulator.selected_actions)
+    pscores = propensity_model.predict_proba(simulator.contexts)
+
+    ipw_learner = IPWLearner(
+        n_actions=env.n_actions,
+        base_classifier=RandomForest(
+            n_estimators=30, min_samples_leaf=10, random_state=12345
+        ),
+    )
+
+    ipw_learner.fit(
+        context=simulator.contexts,
+        action=simulator.selected_actions,
+        reward=simulator.obtained_rewards,
+        pscore=np.choose(simulator.selected_actions, pscores.T),
+    )
+    eval_action_dists = ipw_learner.predict(context=simulator.contexts)
+
+    rewards = np.sum(
+        simulator.ground_truth_rewards * np.squeeze(eval_action_dists, axis=-1)
+    )
+    assert rewards > simulator.total_reward