On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search

Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone
; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1319-1328, 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v48-khandelwal16, 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}, year = {2016}, editor = {Maria Florina Balcan and Kilian Q. Weinberger}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/khandelwal16.pdf}, url = {http://proceedings.mlr.press/v48/khandelwal16.html}, 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.} }
Endnote
%0 Conference Paper %T On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search %A Piyush Khandelwal %A Elad Liebman %A Scott Niekum %A Peter Stone %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-khandelwal16 %I PMLR %J Proceedings of Machine Learning Research %P 1319--1328 %U http://proceedings.mlr.press %V 48 %W PMLR %X 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.
RIS
TY - CPAPER TI - On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search AU - Piyush Khandelwal AU - Elad Liebman AU - Scott Niekum AU - Peter Stone BT - Proceedings of The 33rd International Conference on Machine Learning PY - 2016/06/11 DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-khandelwal16 PB - PMLR SP - 1319 DP - PMLR EP - 1328 L1 - http://proceedings.mlr.press/v48/khandelwal16.pdf UR - http://proceedings.mlr.press/v48/khandelwal16.html AB - 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. ER -
APA
Khandelwal, P., Liebman, E., Niekum, S. & Stone, P.. (2016). On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search. Proceedings of The 33rd International Conference on Machine Learning, in PMLR 48:1319-1328

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