Experience Replay

Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e_{t} = \left(s_{t}, a_{t}, r_{t}, s_{t+1}\right)$ in a data-set $D = e_{1}, \cdots, e_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.

Image Credit: Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran