Experience replay is a reinforcement learning technique in which an agent stores past state transitions in a replay buffer and later samples them for training. It is commonly used in deep Q-learning to reduce temporal correlation and improve learning stability.
In reinforcement learning, a DQN technique used to reduce temporal correlations in training data. The agent stores state transitions in a replay buffer, and then samples transitions from the replay buffer to create training data.