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  Static and Dynamic Values of Computation in MCTS

Sezener, E., & Dayan, P. (submitted). Static and Dynamic Values of Computation in MCTS.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-A8FB-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-E3DF-0
Genre: Paper

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https://arxiv.org/pdf/2002.04335.pdf (Any fulltext)
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 Creators:
Sezener, E, Author
Dayan, P1, 2, Author              
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Monte-Carlo Tree Search (MCTS) is one of the most-widely used methods for planning, and has powered many recent advances in artificial intelligence. In MCTS, one typically performs computations (i.e., simulations) to collect statistics about the possible future consequences of actions, and then chooses accordingly. Many popular MCTS methods such as UCT and its variants decide which computations to perform by trading-off exploration and exploitation. In this work, we take a more direct approach, and explicitly quantify the value of a computation based on its expected impact on the quality of the action eventually chosen. Our approach goes beyond the "myopic" limitations of existing computation-value-based methods in two senses: (I) we are able to account for the impact of non-immediate (ie, future) computations (II) on non-immediate actions. We show that policies that greedily optimize computation values are optimal under certain assumptions and obtain results that are competitive with the state-of-the-art.

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 Dates: 2020-02
 Publication Status: Submitted
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