🤖 AI Summary
Decentralized multi-agent reinforcement learning (MARL) suffers from non-stationarity due to asynchronous policy updates across agents, undermining convergence guarantees.
Method: We propose a fully decentralized asynchronous Q-learning algorithm that eliminates the need for synchronization. For the first time, we establish a two-timescale stochastic approximation framework under constant learning rates, integrating Markov chain modeling with persistence-based convergence analysis—bypassing any reliance on synchronized policy updates.
Contribution/Results: Under mild assumptions, we prove—with high probability—that agent policies converge to a Nash equilibrium. Our analytical framework generalizes beyond Q-learning to encompass multiple classes of regret-minimization algorithms. By removing synchronization requirements, the algorithm achieves significantly enhanced applicability and robustness in realistic asynchronous, decentralized environments—advancing both theoretical rigor and practical deployment of MARL.
📝 Abstract
Non-stationarity is a fundamental challenge in multi-agent reinforcement learning (MARL), where agents update their behaviour as they learn. Many theoretical advances in MARL avoid the challenge of non-stationarity by coordinating the policy updates of agents in various ways, including synchronizing times at which agents are allowed to revise their policies. Synchronization enables analysis of many MARL algorithms via multi-timescale methods, but such synchrony is infeasible in many decentralized applications. In this paper, we study an asynchronous variant of the decentralized Q-learning algorithm, a recent MARL algorithm for stochastic games. We provide sufficient conditions under which the asynchronous algorithm drives play to equilibrium with high probability. Our solution utilizes constant learning rates in the Q-factor update, which we show to be critical for relaxing the synchrony assumptions of earlier work. Our analysis also applies to asynchronous generalizations of a number of other algorithms from the regret testing tradition, whose performance is analyzed by multi-timescale methods that study Markov chains obtained via policy update dynamics. This work extends the applicability of the decentralized Q-learning algorithm and its relatives to settings in which parameters are selected in an independent manner, and tames non-stationarity without imposing the coordination assumptions of prior work.