Policy Distribution

Policy distribution in reinforcement learning focuses on characterizing and manipulating the probability distribution over actions taken by an agent, aiming to optimize performance and robustness. Current research emphasizes developing methods to learn and represent multimodal policy distributions, employing techniques like diffusion models and Boltzmann exploration within frameworks such as Monte Carlo Tree Search and Proximal Policy Optimization. These advancements address challenges like efficient exploration, robust policy evaluation under covariate shift, and safe policy learning under constraints, impacting both theoretical understanding and practical applications in areas such as robotics and online advertising. Improved policy distribution modeling leads to more efficient and reliable reinforcement learning agents.