Prioritized Experience Replay
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2015
- DOI:
- 10.48550/arXiv.1511.05952
- arXiv:
- arXiv:1511.05952
- Bibcode:
- 2015arXiv151105952S
- Keywords:
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- Computer Science - Machine Learning
- E-Print:
- Published at ICLR 2016