# Improving Experience Replay with Successor Representation

arXiv: https://arxiv.org/abs/2111.14331 · HTML: https://mlsanity.com/paper/2111.14331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Dopamine: A Research Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.06110/) (2018)
- [Deep Successor Reinforcement Learning](https://mlsanity.com/paper/1606.02396/) (2016)
- [Prioritized Sequence Experience Replay](https://mlsanity.com/paper/1905.12726/) (2019)

## Abstract (arXiv)

Prioritized experience replay is a reinforcement learning technique whereby agents speed up learning by replaying useful past experiences. This usefulness is quantified as the expected gain from replaying the experience, a quantity often approximated as the prediction error (TD-error). However, recent work in neuroscience suggests that, in biological organisms, replay is prioritized not only by gain, but also by "need" -- a quantity measuring the expected relevance of each experience with respect to the current situation. Importantly, this term is not currently considered in algorithms such as prioritized experience replay. In this paper we present a new approach for prioritizing experiences for replay that considers both gain and need. Our proposed algorithms show a significant increase in performance in benchmarks including the Dyna-Q maze and a selection of Atari games.