Abstract
This paper focuses on the selection phase of Monte- Carlo Tree Search (MCTS). We define batch value of perfect information (BVPI) in game trees as a generalization of value of computation as proposed by Russell and Wefald, and use it for selecting nodes to sample in MCTS. We show that computing the BVPI is NP-hard, but it can be approximated in polynomial time. In addition, we propose methods that intelligently find sets of fringe nodes with high BVPI, and quickly select nodes to sample from these sets. We apply our new BVPI methods to partial game trees, both in a stand-alone set of tests, and as a component of a full MCTS algorithm. Empirical results show that our BVPI methods outperform existing node-selection methods for MCTS in different scenarios.
| Original language | English |
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| State | Published - 2017 |
| Externally published | Yes |
| Event | 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia Duration: 11 Aug 2017 → 15 Aug 2017 |
Conference
| Conference | 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 |
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| Country/Territory | Australia |
| City | Sydney |
| Period | 11/08/17 → 15/08/17 |
Bibliographical note
Funding Information:Supported by ISF grant 417/13, and by the Frankel Center. We thank the authors of [Justesen et al., 2014; Eyerich et al., 2010] for providing their code.
Funding
Supported by ISF grant 417/13, and by the Frankel Center. We thank the authors of [Justesen et al., 2014; Eyerich et al., 2010] for providing their code.
| Funders | Funder number |
|---|---|
| Frankel Center | |
| Israel Science Foundation | 417/13 |