Core Concepts
PSRL-ZSG algorithm achieves a Bayesian regret bound of eO(HS√AT) in zero-sum stochastic games with an arbitrary opponent.
Abstract
The paper introduces the PSRL-ZSG algorithm for zero-sum stochastic games with an arbitrary opponent. It discusses the challenges of multi-agent reinforcement learning and the theoretical advancements in this field. The algorithm achieves a Bayesian regret bound and improves upon existing results. The content is structured as follows:
Introduction to competitive reinforcement learning and the challenges of multi-agent RL.
Overview of self-play algorithms and the limitations of theoretical understanding.
Proposal of the PSRL-ZSG algorithm for online learning against arbitrary opponents.
Detailed explanation of the algorithm and its theoretical analysis.
Comparison with existing algorithms and improvements in regret bounds.
Related literature on stochastic games and exploration in single-agent RL.
Preliminaries and assumptions for the analysis.
Proof of Theorem 3.1 and analysis of regret bounds.
Conclusion and implications of the PSRL-ZSG algorithm.
Stats
PSRL-ZSG 알고리즘은 eO(HS√AT)의 Bayesian 후회 한계를 달성합니다.
Quotes
"PSRL-ZSG algorithm achieves a Bayesian regret bound of eO(HS√AT) in zero-sum stochastic games with an arbitrary opponent."