State Action Distribution

State-action distribution, central to reinforcement learning, describes the probability of an agent taking specific actions in different states. Current research focuses on mitigating the challenges of distribution shifts between training and deployment data, particularly in offline reinforcement learning, employing techniques like data augmentation, uncertainty-guided exploration, and distribution matching methods (e.g., using normalizing flows or relaxed f-divergences). These advancements aim to improve the generalization and robustness of reinforcement learning agents, leading to more reliable and efficient learning from limited or imperfect datasets, with applications in robotics and game AI.