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arXiv · 1511.05952

Prioritized Experience Replay

Tom Schaul·John Quan·Ioannis Antonoglou·David Silver
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Abstract

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

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Tom Schaul, John Quan, Ioannis Antonoglou, David Silver. 2016-02-25. Prioritized Experience Replay. https://arxiv.org/abs/1511.05952

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