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On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search.
Piyush
Khandelwal, Elad Liebman, Scott
Niekum, and Peter Stone.
In Proceedings of The 33rd International
Conference on Machine Learning, pp. 1319–1328, June 2016.
Over the past decade, Monte Carlo Tree Search (MCTS) and specifically Upper Confidence Bound in Trees (UCT) have proven to be quite effective in large probabilistic planning domains. In this paper, we focus on how values are backpropagated in the MCTS tree, and apply complex return strategies from the Reinforcement Learning (RL) literature to MCTS, producing 4 new MCTS variants. We demonstrate that in some probabilistic planning benchmarks from the International Planning Competition (IPC), selecting a MCTS variant with a backup strategy different from Monte Carlo averaging can lead to substantially better results. We also propose a hypothesis for why different backup strategies lead to different performance in particular environments, and manipulate a carefully structured grid-world domain to provide empirical evidence supporting our hypothesis.
@inproceedings{ICML16-khandelwal,
title={On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search},
author={Piyush Khandelwal and Elad Liebman and Scott Niekum and Peter Stone},
booktitle={Proceedings of The 33rd International Conference on Machine Learning},
pages={1319--1328},
location={New York City, NY, USA},
month={June},
year={2016},
abstract = {Over the past decade, Monte Carlo Tree Search (MCTS) and
specifically Upper Confidence Bound in Trees (UCT) have proven to be quite
effective in large probabilistic planning domains. In this paper, we focus on
how values are backpropagated in the MCTS tree, and apply complex return
strategies from the Reinforcement Learning (RL) literature to MCTS, producing 4
new MCTS variants. We demonstrate that in some probabilistic planning
benchmarks from the International Planning Competition (IPC), selecting a
MCTS variant with a backup strategy different from Monte Carlo averaging can
lead to substantially better results. We also propose a hypothesis for why
different backup strategies lead to different performance in particular
environments, and manipulate a carefully structured grid-world domain to
provide empirical evidence supporting our hypothesis.},
}
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