XBBO/examples/LaMCTS

• LaMCTS
• • Introduction
  • Abstract
  • Usage
  • benchmark
  • Results
  • • Branin

LaMCTS

Introduction

Latent Action Monte Carlo Tree Search (LA-MCTS)

Abstract

Since LaNAS works very well on NAS datasets, e.g. NASBench-101, and the core of the algorithm can be easily generalized to other problems, we extend it to be a generic solver for black-box function optimization. LA-MCTS further improves by using a nonlinear classifier at each decision node in MCTS and use a surrogate (e.g., a function approximator) to evaluate each sample in the leaf node. The surrogate can come from any existing Black-box optimizer (e.g., Bayesian Optimization). The details of LA-MCTS can be found in the following paper.

Usage

E.g. PYTHONPATH='./' python examples/LaMCTS/rosenbrock_LaMCTS.py

benchmark

Modify the following section of comparison/xbbo_benchmark.py :

test_algs = ["lamcts"]

And run PYTHONPATH='./' python comparison/xbbo_benchmark.py in the command line.

Results

Branin

Method	Minimum	Best minimum	Mean f_calls to min	Std f_calls to min	Fastest f_calls to min
XBBO(lamcts)	0.440+/-0.040	0.407	81.7	39.666	4