Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00223904" target="_blank" >RIV/68407700:21230/14:00223904 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-319-05428-5_3" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-05428-5_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-05428-5_3" target="_blank" >10.1007/978-3-319-05428-5_3</a>

Result language
angličtina
Original language name
Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel
Original language description
Monte Carlo Tree Search (MCTS) has become a widely popular sampled-based search algorithm for two-player games with perfect information. When actions are chosen simultaneously, players may need to mix between their strategies. In this paper, we discuss the adaptation of MCTS to simultaneous move games. We introduce a new algorithm, Online Outcome Sampling (OOS), that approaches a Nash equilibrium strategy over time. We compare both head-to-head performance and exploitability of several MCTS variants inGoofspiel. We show that regret matching and OOS perform best and that all variants produce less exploitable strategies than UCT.
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F12%2F2054" target="_blank" >GAP202/12/2054: Security Games in Extensive Form</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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