AI RESEARCH
GAE Falls Short in Imperfect-Information Self-Play Reinforcement Learning
arXiv CS.LG
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ArXi:2605.19235v1 Announce Type: new Competitive multi-agent reinforcement learning in imperfect-information games requires agents to act under partial observability and against adversarial opponents, necessitating stochastic policies. While self-play reinforcement learning with Proximal Policy Optimization (PPO) has achieved strong empirical success, its standard advantage estimator, generalized advantage estimation, suffers from additional variance due to the sampling of stochastic future actions.