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Prioritized Experience Replay
[article]
2016
arXiv
pre-print
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
arXiv:1511.05952v4
fatcat:mcttbjzpsvhhrkcupyt2cksqai