Behavior Policy

Behavior policy in reinforcement learning focuses on designing and optimizing the policy used to collect data for training and evaluating other policies (target policies). Current research emphasizes improving the efficiency and robustness of behavior policies, particularly through techniques like adaptive regularization, multi-objective optimization, and tailored designs informed by offline data or expert demonstrations. These advancements aim to reduce the variance of policy estimators, mitigate out-of-distribution issues in offline reinforcement learning, and enhance sample efficiency, ultimately leading to more reliable and efficient training of reinforcement learning agents across various applications. The impact extends to both theoretical understanding of reinforcement learning algorithms and practical improvements in real-world applications like robotics and autonomous systems.