Robust Federated Q-Learning with Almost No Communication
We consider a federated reinforcement learning setting involving $M$ agents, all of whom interact with a common Markov Decision Process (MDP). The agents exchange information via a central server to learn the optimal value function. Our goal is to understand to what extent one can hope for collaborative sample-complexity speedups in such a setting, when a small fraction of the agents are adversarial and can act arbitrarily. To that end, we propose Robust Fed-Q}, a federated Q-learning algorithm that blends ideas from both model-based and model-free RL, along with the median-of-means device from robust statistics. We prove that despite corruption, with high-probability, Robust Fed-Q (i) gu...
| Authors | Sreejeet Maity, Aritra Mitra |
|---|---|
| Primary category | cs.LG |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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