Learning opening books in partially observable games: using random seeds in Phantom Go

2016-07-08 · Tristan Cazenave, Jialin Liu, Fabien Teytaud, Olivier Teytaud

Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is far from being negligible. Then, we apply two different existing algorithms for selecting good seeds and good probability distributions over seeds. This mainly leads to learning an opening book. We apply this to Phantom Go, which, as all phantom games, is hard for opening book learning. We improve the winning rate from 50% to 70% in 5x5 against the same AI, and from approximately 0% to 40% in 5x5, 7x7 and 9x9 against a stronger (learning) opponent.

📄 PDF Abstract BibTeX arXiv:1607.02431

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Learning to Cooperate via Policy Search

2014-08-07 · Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, Leslie Pack Kaelbling

Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they o…

Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

HSVI can solve zero-sum Partially Observable Stochastic Games

2022-10-26 · Aurélien Delage, Olivier Buffet, Jilles S. Dibangoye, Abdallah Saffidine

State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on linear programming or regret minimization, though not on dynamic programming (DP) or heuristic search (HS), while the latter are …

Decision Making Heuristic Search Open-Ended Question Answering Sequential Decision Making

Bayesian Model of Behaviour in Economic Games

2008-12-01 · NeurIPS 2008 12 · Debajyoti Ray, Brooks King-Casas, P. R. Montague, Peter Dayan

Classical Game Theoretic approaches that make strong rationality assumptions have difficulty modeling observed behaviour in Economic games of human subjects. We investigate the role of finite levels of iterated reasoning…

model

Minimax-Optimal Policy Regret in Partially Observable Markov Games

2026-06-01 · Raman Arora arxiv

We study sequential decision-making in partially observable environments against strategic, adaptive opponents, modeled as partially observable Markov games (POMGs). The central challenge is to learn latent dynamics from…

Partially Observable Stochastic Games with Neural Perception Mechanisms

2023-10-17 · Rui Yan, Gabriel Santos, Gethin Norman, David Parker 외

Stochastic games are a well established model for multi-agent sequential decision making under uncertainty. In practical applications, though, agents often have only partial observability of their environment. Furthermor…

Decision Making Decision Making Under Uncertainty Sequential Decision Making