Experience replay is a technique used in deep reinforcement learning (DRL) to improve the stability and efficiency of training by storing and reusing past experiences. In DRL, an agent learns by interacting with an environment, collecting data in the form of state-action-reward-next-state tuples. Without experience replay, the agent would train only on the most recent experiences, which can lead to unstable learning due to correlated data and inefficiency in leveraging rare but important events. Experience replay addresses these issues by maintaining a buffer of past experiences, allowing the agent to sample and learn from them multiple times.
One key benefit of experience replay is breaking the temporal correlation between consecutive experiences. When an agent interacts with an environment, the data it collects is sequential and highly dependent on its current policy. Training directly on this stream of data can cause neural networks to overfit to recent experiences, leading to unstable updates. By randomly sampling from a buffer of past experiences—a process called "minibatch sampling"—the agent learns from a more diverse and decorrelated set of examples. For instance, in Deep Q-Networks (DQN), a foundational DRL algorithm, experience replay ensures that the network isn’t biased by the order of experiences, improving the likelihood of converging to an optimal policy. This approach also makes better use of hardware resources, as batches of uncorrelated data enable more efficient parallel processing on GPUs.
Another critical role of experience replay is improving sample efficiency and mitigating catastrophic forgetting. Reinforcement learning often requires vast amounts of data, and reusing experiences allows the agent to learn more from each interaction. For example, in a complex environment like training a robot to walk, rare successes (e.g., maintaining balance) might occur infrequently. Storing these experiences ensures the agent can revisit and reinforce those behaviors. Additionally, experience replay helps prevent the neural network from forgetting earlier lessons. Without a buffer, an agent might discard valuable past knowledge as it updates its policy, leading to performance regressions. By balancing old and new data—such as using a prioritized replay buffer to emphasize impactful experiences—the agent maintains a more stable and robust learning process. This balance is especially crucial in real-world applications like autonomous driving, where rare but critical scenarios (e.g., avoiding collisions) must be retained and learned from effectively.