Experience replay is a technique used in deep reinforcement learning (DRL) to improve the efficiency and stability of training. It involves storing an agent's experiences (state, action, reward, next state) in a replay buffer and then sampling from this buffer to train the model. This process helps break the correlation between consecutive experiences, which can lead to more stable training by allowing the agent to learn from a broader and more diverse set of experiences.
The key benefit of experience replay is that it allows the agent to revisit previous experiences multiple times, reinforcing learning from those interactions. This is particularly important in environments where interactions are costly or time-consuming. By sampling random batches of experiences, the model can learn more generalizable patterns.
In practice, experience replay is often used in combination with Q-learning or Deep Q-networks (DQNs), where the agent uses a neural network to approximate the Q-values and improves its performance by revisiting old experiences during training.