Robust Policy

Robust policy learning in reinforcement learning (RL) focuses on developing agents capable of performing well despite uncertainties and disturbances in their environment, a crucial step for deploying RL in real-world settings. Current research emphasizes techniques like adversarial training, curriculum learning, and the use of factored state representations to improve robustness, often employing model architectures such as deep Q-networks, actor-critic methods, and transformers. These advancements are significant because they address the critical challenge of generalization and reliability in RL, paving the way for safer and more dependable autonomous systems across various applications.