Action Distribution

Action distribution in reinforcement learning focuses on characterizing and manipulating the probability distribution of actions taken by an agent, aiming to improve learning efficiency, robustness, and safety. Current research emphasizes methods like diffusion models and Wasserstein distance-based regularizers to shape action distributions, particularly for handling multimodality and mitigating issues like reward hacking and distributional shifts in offline learning. These advancements are crucial for enabling effective reinforcement learning in complex real-world scenarios, such as robotic manipulation and multi-agent coordination, where carefully managing action selection is paramount for success.