Replay Buffer
A replay buffer is a data structure used in reinforcement learning to store and sample past experiences (state, action, reward, next state) for training machine learning models. It enables algorithms to learn from a diverse set of historical interactions, improving stability and efficiency by breaking temporal correlations in sequential data. This technique is fundamental in off-policy reinforcement learning methods, such as Deep Q-Networks (DQN), to facilitate experience replay.
Developers should learn about replay buffers when working on reinforcement learning projects, especially for training agents in environments with complex state spaces or sparse rewards. It is crucial for improving sample efficiency and preventing catastrophic forgetting in neural networks by allowing models to revisit and learn from past experiences multiple times. Use cases include robotics control, game AI (e.g., Atari games), and autonomous systems where data collection is expensive or time-consuming.