mlsanityData to 7 Oct 2026

Reverse Experience Replay

arXiv 1910.08780 ↗First posted 19 Oct 2019cs.LG cs.AI stat.MLcited by 8

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Abstract (arXiv)

This paper describes an improvement in Deep Q-learning called Reverse Experience Replay (also RER) that solves the problem of sparse rewards and helps to deal with reward maximizing tasks by sampling transitions successively in reverse order. On tasks with enough experience for training and enough Experience Replay memory capacity, Deep Q-learning Network with Reverse Experience Replay shows competitive results against both Double DQN, with a standard Experience Replay, and vanilla DQN. Also, RER achieves significantly increased results in tasks with a lack of experience and Replay memory capacity.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 2.

8 papers.