Robustness and Generalization in Reinforcement Learning

The field of reinforcement learning is moving towards developing more robust and generalizable methods. Recent research has focused on addressing challenges such as corruption in data, partial observability, and generalization in complex environments. Innovations in algorithms and techniques have enabled agents to learn effective policies in the presence of uncertainty and adversity. Notable contributions include the development of corruption-robust performative reinforcement learning, point-based algorithms for distributional reinforcement learning, and online feedback-efficient active target discovery methods. Noteworthy papers include:

  • The paper on corruption-robustness in performative reinforcement learning, which proposes a novel approach to handle corrupted data.
  • The paper on distributional reinforcement learning in partially observable domains, which introduces new distributional Bellman operators and a point-based algorithm for efficient planning.
  • The paper on online feedback-efficient active target discovery, which presents a diffusion-guided method for efficient target discovery in partially observable environments. These advancements have the potential to significantly impact the development of more reliable and effective reinforcement learning systems.

Sources

On Corruption-Robustness in Performative Reinforcement Learning

A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains

Online Feedback Efficient Active Target Discovery in Partially Observable Environments

Generalization in Monitored Markov Decision Processes (Mon-MDPs)

Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning

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