Latent Action Monte Carlo Tree Search (LA-MCTS)
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.
E.g. PYTHONPATH='./' python examples/LaMCTS/rosenbrock_LaMCTS.py
Modify the following section of comparison/xbbo_benchmark.py :
test_algs = ["lamcts"]And run PYTHONPATH='./' python comparison/xbbo_benchmark.py in the command line.
| 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 |