Independent and Decentralized Learning in Markov Potential Games

• 成果类型: Article
• 署名作者: Maheshwari, Chinmay; Wu, Manxi; Pai, Druv; Sastry, Shankar
• 署名单位: University of California System; University of California Berkeley; University of California System; University of California Berkeley
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3576379
• 发表日期: 2025
• 关键词: Fictitious play; CONVERGENCE

摘要

We study a multiagent reinforcement learning dynamics and analyze its asymptotic behavior in infinite-horizon discounted Markov potential games. We focus on the independent and decentralized setting, where players do not know the game parameters and cannot communicate or coordinate. In each stage, players update their estimate of Q-function that evaluates their total contingent payoff based on the realized one-stage reward in an asynchronous manner. Then, players independently update their policies by incorporating an optimal one-stage deviation strategy based on the estimated Q-function. Inspired by the actor-critic algorithm in single-agent reinforcement learning, a key feature of our learning dynamics is that agents update their Q-function estimates at a faster timescale than the policies. Leveraging tools from two-timescale asynchronous stochastic approximation theory, we characterize the convergent set of learning dynamics.