LA-MCTS

The code is based of paper Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search.

Component

LA-MCTS has three major components:

Main Loop

At each iteration, main loop builds the Monte Carlo search tree, selects next node, and samples on selected nodes.

Classifier

Classifiers define rules to split a node, they also predict if a sample belongs to the node. Currently there are several builtin classifiers:

SVM Based Classifiers

In these classifiers, some cluster algorithm is used to label the samples, then SVM is used to classify the samples. Builtin cluster algorithms include:

• KMeans
• Threshold
• Linear regression

Regression Classifier

A regressor is used to fit samples, then a threshold (median or mean) is used to separate them.

Samplers

Samplers draw samples in node space. Currently builtin samplers include:

• Random sampler
• Bayesian sampler
• TuRBO sampler
• CMAES sampler
• Nevergrad sampler

Users may provide their own classifier and/or sampler by implementing Classifier and Sampler interface.

Usage

An example can be found at example_opt.py.

Docs

Detailed docs can found at here.

License

LA-MCTS is under CC-BY-NC 4.0 license.