Independent and Decentralized Learning in Markov Potential Games
This addresses the challenge of decentralized learning in multi-agent systems, but it is incremental as it builds on existing actor-critic methods and stochastic approximation theory.
The paper tackles the problem of multi-agent reinforcement learning in Markov potential games under independent and decentralized conditions, where agents learn without knowing game parameters or coordinating, and shows that the learning dynamics converge to a characterized set.
We study a multi-agent 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.