The field of reinforcement learning is moving towards developing more robust and adaptive algorithms that can handle real-world challenges such as corruption, noise, and failures. Researchers are exploring innovative approaches to address these issues, including the use of information-theoretic frameworks, distributionally robust methods, and hierarchical decentralized control. These advances have the potential to enable reinforcement learning agents to operate effectively in complex and dynamic environments. Notable papers in this area include:

• Corruption-Tolerant Asynchronous Q-Learning with Near-Optimal Rates, which proposes a provably robust variant of Q-learning that operates effectively even when a fraction of the observed rewards are arbitrarily perturbed by an adversary.
• Mutual Information Tracks Policy Coherence in Reinforcement Learning, which presents an information-theoretic framework for diagnosing deployment-time anomalies in reinforcement learning agents.