# A Short Survey On Memory Based Reinforcement Learning

arXiv: https://arxiv.org/abs/1904.06736 · HTML: https://mlsanity.com/paper/1904.06736/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2019.

## Built on by (most cited first, 9 of 9)

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- [Memento 2: Learning by Stateful Reflective Memory](https://mlsanity.com/paper/2512.22716/) (2025)
- [Sequential memory improves sample and memory efficiency in Episodic Control](https://mlsanity.com/paper/2112.14734/) (2021)
- [Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning](https://mlsanity.com/paper/2101.00687/) (2021)
- [D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning](https://mlsanity.com/paper/2110.15089/) (2021)
- [TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments](https://mlsanity.com/paper/2606.24712/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) is a branch of machine learning which is employed to solve various sequential decision making problems without proper supervision. Due to the recent advancement of deep learning, the newly proposed Deep-RL algorithms have been able to perform extremely well in sophisticated high-dimensional environments. However, even after successes in many domains, one of the major challenge in these approaches is the high magnitude of interactions with the environment required for efficient decision making. Seeking inspiration from the brain, this problem can be solved by incorporating instance based learning by biasing the decision making on the memories of high rewarding experiences. This paper reviews various recent reinforcement learning methods which incorporate external memory to solve decision making and a survey of them is presented. We provide an overview of the different methods - along with their advantages and disadvantages, applications and the standard experimentation settings used for memory based models. This review hopes to be a helpful resource to provide key insight of the recent advances in the field and provide help in further future development of it.