Replay Buffer

A replay buffer is a memory structure in reinforcement learning that stores past experiences (state-action-reward-next state tuples) for later reuse during training. Current research focuses on improving replay buffer efficiency and effectiveness, including developing novel sampling strategies (e.g., prioritizing informative samples, using curiosity-driven selection, or incorporating topological relationships between experiences) and designing buffer architectures that mitigate catastrophic forgetting in continual learning. These advancements aim to enhance sample efficiency, reduce memory requirements, and improve the performance of reinforcement learning algorithms across various tasks and environments, particularly in scenarios with sparse rewards or continuous data streams.