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.
Papers
November 27, 2024
October 25, 2024
October 23, 2024
October 22, 2024
October 15, 2024
October 9, 2024
October 8, 2024
September 29, 2024
September 13, 2024
August 8, 2024
August 6, 2024
July 4, 2024
June 26, 2024
June 24, 2024
May 19, 2024
May 18, 2024
May 2, 2024
April 22, 2024
April 7, 2024