# Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means

arXiv: https://arxiv.org/abs/1911.09560 · HTML: https://mlsanity.com/paper/1911.09560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Successor Feature Neural Episodic Control](https://mlsanity.com/paper/2111.03110/) (2021)
- [Structure-Induced Information for Rerooting Levin Tree Search](https://mlsanity.com/paper/2605.30664/) (2026)

## Abstract (arXiv)

Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-parametric models to estimate the value function, they learn rapidly, retrieving cached values from similar past states. In realistic scenarios, with limited resources and noisy data, maintaining meaningful representations in memory is essential to speed up the learning and avoid catastrophic forgetting. Unfortunately, EC methods have a large space and time complexity. We investigate different solutions to these problems based on prioritising and ranking stored states, as well as online clustering techniques. We also propose a new dynamic online k-means algorithm that is both computationally-efficient and yields significantly better performance at smaller memory sizes; we validate this approach on classic reinforcement learning environments and Atari games.