Robust Asynchronous Q-Learning under Reward and State Corruption via Batching
Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feedback. Specifically, at each time-step, an adversary can perturb both the reward and state observations of the learner following the Huber contamination model. To defend against such data corruption, we propose BR-Async-Q: a novel, epoch-based, robust Q-learning algorithm built upon two key ideas: (i) partitioning the online data stream into batches to reduce variance, and (ii) constructing robust estimates of the Bellman optimality operator using such batched data. We prove a high-probability $\ell_\infty$ error bound for BR-Async-Q that m...
| Authors | Sreejeet Maity, Aritra Mitra |
|---|---|
| Primary category | cs.LG |
| Published | 23 July 2026 |
| Last updated at source | 24 July 2026 |
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