# Learning from the memory of Atari 2600

arXiv: https://arxiv.org/abs/1605.01335 · HTML: https://mlsanity.com/paper/1605.01335/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2016.

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

- [OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments](https://mlsanity.com/paper/2306.08649/) (2023)
- [Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning](https://mlsanity.com/paper/2106.09256/) (2021)
- [Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks](https://mlsanity.com/paper/2402.06912/) (2024)

## Abstract (arXiv)

We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen. As the benchmark we used the convolutional model proposed in NIPS and received comparable results in all considered games. Quite surprisingly, in the case of Seaquest we were able to train RAM-only agents which behave better than the benchmark screen-only agent. Mixing screen and RAM did not lead to an improved performance comparing to screen-only and RAM-only agents.