“Prioritized Experience Replay”, Tom Schaul, John Quan, Ioannis Antonoglou, David Silver2015-11-18 (model-free RL; similar)⁠:

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their importance.

In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari ALE games.

DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41⁄49 games.

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