RLC 2026
A Finite-Time Analysis of Distributed Q-Learning
Han-Dong Lim, Donghwan Lee
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 under tabular lookup setting for Markovian observation model.