Variance-Reduced Q-Learning over Static and Time-Varying Networks
We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange information over a network to collectively learn the optimal state-action value function. For this setting, we introduce a novel epoch-based distributed $Q$-learning algorithm called VRDQ, where within each epoch, agents locally estimate the Bellman optimality operator and diffuse information using a consensus-based protocol. For both static and time-varying networks, we establish high-probability finite-time convergence rates for VRDQ that enjoy linear speedups from collaboration. Crucially, we prove that such speedups ...
| Authors | Sreejeet Maity, Feng Zhu, Aritra Mitra et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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