Enhancing Playout Policy Adaptation for General Game Playing
Sironi, Chiara; Cazenave, Tristan; Winands, Mark (2021), Enhancing Playout Policy Adaptation for General Game Playing, in Cazenave, Tristan; Teytaud, Olivier; Winands, Mark H. M., Monte Carlo Search, Springer, p. 116-139. 10.1007/978-3-030-89453-5_9
TypeCommunication / Conférence
Conference titleFirst Workshop, MCS 2020, Held in Conjunction with IJCAI 2020
Book titleMonte Carlo Search
Book authorCazenave, Tristan; Teytaud, Olivier; Winands, Mark H. M.
Number of pages141
MetadataShow full item record
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)Playout Policy Adaptation (PPA) is a state-of-the-art strategy that has been proposed to control the playouts in Monte-Carlo Tree Search (MCTS). PPA has been successfully applied to many two-player, sequential-move games. This paper further evaluates this strategy in General Game Playing (GGP) by first reformulating it for simultaneous-move games. Next, it presents five enhancements for the strategy, four of which have been previously successfully applied to a related MCTS playout strategy, the Move-Average Sampling Technique (MAST). Experiments on a heterogeneous set of games show three enhancements to have a positive effect on PPA: (i) updating the policy for all players proportionally to their payoffs instead of updating only the policy of the winner, (ii) collecting statistics for N-grams of moves instead of single moves only, and (iii) discounting the backpropagated payoffs depending on the depth of the playout. Results also show enhanced PPA variants to be competitive with MAST for small search budgets, and better for larger search budgets. The use of an ϵ -greedy selection of moves and of after-move decay of statistics, instead, seem to have a detrimental effect on PPA.
Subjects / KeywordsMonte-Carlo Tree Search; Playout policy adaptation; General game playing
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