LA-MCTS

Latent Action Monte Carlo Tree Search (LA-MCTS)

LA-MCTS is a meta-algortihm that partitions the search space for black-box optimizations. LA-MCTS progressively learns to partition and explores promising regions in the search space, so that solvers such as Bayesian Optimizations (BO) can focus on promising subregions, mitigating the over-exploring issue in high-dimensional problems.

Please reference the following publication when using this package. ArXiv link.

@article{wang2020learning,
  title={Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search},
  author={Wang, Linnan and Fonseca, Rodrigo and Tian, Yuandong},
  journal={NeurIPS},
  year={2020}
}

Run LaMCTS and Baselines in test functions (1 minute tutorial)

For 1 minute evaluation of Bayesian Optimizations (BO) and Evolutionary Algorithm (EA) v.s. LA-MCTS boosted BO, please follow the procedures below.

Here we test on Ackley or Levy in 10 dimensions; Please run multiple times to compare the average performance.

• Evaluate LA-MCTS boosted Bayesian Optimization: using GP surrogate, EI acuqusition, plus LA-MCTS.

cd LA-MCTS
python run.py --func ackley --dims 10 --iterations 100

• Evaluate Bayesian Optimization: using GP surrogate, EI acuqusition.

pip install scikit-optimize
cd LA-MCTS-baselines/Bayesian-Optimization
python run.py --func ackley --dims 10 --iterations 100

• Evaluate Evolutionary Algorithm: using NGOpt from Nevergrad.

pip install nevergrad
cd LA-MCTS-baselines/Nevergrad
python run.py --func ackley --dims 10 --iterations 100

How to use LA-MCTS to optimize your own function?

Please wrap your function into a class defined as follows; functions/functions.py provides a few examples.

class myFunc:
    def __init__(self, dims=1):
        self.dims    = dims                   #problem dimensions
        self.lb      =  np.ones(dims)         #lower bound for each dimensions 
        self.ub      =  np.ones(dims)         #upper bound for each dimensions 
        self.tracker = tracker('myFunc')      #defined in functions.py

    def __call__(self, x):
        # some sanity check of x        
        f(x) = myFunc(x)
        self.tracker.track( f(x), x )        
        return f(x)

After defining your function, e.g. f = func(), minimizing f(x) is as easy as passing f into MCTS.

f = myFunc()
agent = MCTS(lb = f.lb,     # the lower bound of each problem dimensions
             ub = f.ub,     # the upper bound of each problem dimensions
             dims = f.dims, # the problem dimensions
             ninits = 40,   # the number of random samples used in initializations 
             func = f       # function object to be optimized
             )
agent.search(iterations = 100)

Please check run.py.

What it can and cannot do?

In this release, the codes only support optimizing continuous black box functions.

Tuning LA-MCTS

Cp: controling the amount of exploration, MCTS.py line 27.

We set Cp = 0.1 * max of f(x) .

For example, if f(x) measures the test accuracy of a neural network x, Cp = 0.1. But the best performance should be tuned in specific cases. A large Cp encourages LA-MCTS to visit bad regions more often (exploration), and a small Cp otherwise. LA-MCTS degenreates to random search if Cp = 0, while LA-MCTS degenerates to a pure greedy based policy, e.g. regression tree, at Cp = 0. Both are undesired.

Leaf Size: the splitting threshold (θ), MCTS.py line 38.

We set θ ∈ [20, 100] in our experiments.

the splitting threshold controls the speed of tree growth. Given the same #samples, smaller θ leads to a deeper tree. If Ω is very large, more splits enable LA-MCTS to quickly focus on a small promising region, and yields good results. However, if θ is too small, the performance and the boundary estimation of the region become more unreliable.

SVM kernel: the type of kernels used by SVM, Classifier.py line 35.

kernel can be 'linear', 'poly', 'rbf'

From our experiments, linear kernel is the fastest, but rbf or poly are generally producing better results. If you want to draw > 1000 samples, we suggest using linear kernel, and rbf and poly otherwise.

Mujoco tasks and Gym Games.

• Run Lunarlanding:

1. Install gym and start running.

pip install gym
python run.py --func lunar --samples 500

Copy the final "current best x" from the output to visualize your policy.

2. Visualize your policy

cd functions
Replace our policy value to your learned policy from the previous step, i.e. policy = np.array([xx]).
python visualize_policy.py

• Run MuJoCo:

     

1. Setup mujoco-py, see here.

2. Download TuRBO from here. Extract and find the turbo folder.

3. Move revision.patch to the turbo folder,

cp revision.patch TuRBO-master/turbo
cd TuRBO-master/turbo
patch -p1 < revision.patch
cd ../..
mv TuRBO-master/turbo ./turbo_1

4. Open file Classifier.py, uncomment line 23, 343->368. Open file MCTS.py, change line 47, solver_type from bo to turbo.

5. Now it is ready to run,

python run.py --func swimmer --iterations 1000

Possible Extensions

In MCTS.py line 229, the select returns a path and a leaf to bound the sampling space. We used get_sample_ratio_in_region in Classifier.py to acquire samples in the selected partition. Other sampler can also be used.

LAMCTS can be used together with any value function / evaluator / cost models.