Policy Entropy

Policy entropy, a measure of the randomness in an agent's actions within a reinforcement learning framework, is a key focus in improving the efficiency and robustness of learning algorithms. Current research emphasizes methods to control and optimize policy entropy, often using techniques like entropy regularization within algorithms such as Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), and exploring novel approaches like extremum-seeking control to guide action selection. These advancements aim to enhance exploration, mitigate overfitting to imperfect reward models, and improve the stability and generalization of reinforcement learning agents across diverse applications, including robotics and personalized systems.