A Finite-Time Analysis of Distributed Q-Learning

Han-Dong Lim, Donghwan Lee

Keywords: reinforcement learning, multi-agent, distributed learning, q-learning

Abstract: Multi-agent reinforcement learning (MARL) has witnessed a remarkable surge in interest, fueled by the empirical success achieved in applications of single-agent reinforcement learning (RL). In this study, we consider a distributed Q-learning scenario, wherein a number of agents cooperatively solve a sequential decision making problem without access to the central reward function which is an average of the local rewards. In particular, we study finite-time analysis of a distributed Q-learning algorithm, and provide a new sample complexity result of $\tilde{\mathcal{O}}\left( \max\left\\\{ \frac{1}{\epsilon^2}\frac{t_{\text{mix}}}{(1-\gamma)^6 d_{\min}^4 } ,\frac{1}{\epsilon}\frac{\sqrt{|\mathcal{S}||\mathcal{A}|}}{(1-\sigma_2(\boldsymbol{W}))(1-\gamma)^4 d_{\min}^3} \right\\\} \right)$ under tabular lookup setting for Markovian observation model.

Primary Area: reinforcement learning

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Submission Number: 10243