# A Comparison of learning algorithms on the Arcade Learning Environment

arXiv: https://arxiv.org/abs/1410.8620 · HTML: https://mlsanity.com/paper/1410.8620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2014.

## Built on by (most cited first, 1 of 1)

- [Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning](https://mlsanity.com/paper/2101.00687/) (2021)

## Abstract (arXiv)

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We then provide a comparison of model-free, linear learning algorithms on this challenging problem set.