Efficient Diversity-Based Experience Replay for Deep Reinforcement Learning

A revised arXiv paper introduces an experience replay method for deep reinforcement learning that prioritizes diversity when sampling past experiences. The authors argue that conventional uniform and prioritized replay strategies often use stored transitions inefficiently, and their approach aims to improve learning efficiency by selecting a more varied set of experiences. The updated version is cross-listed in the cs.AI and cs.LG categories.