# Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1905.02825 · HTML: https://mlsanity.com/paper/1905.02825/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

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

- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems](https://mlsanity.com/paper/2106.03775/) (2021)
- [From Heatmaps to Structural Explanations of Image Classifiers](https://mlsanity.com/paper/2109.06365/) (2021)

## Abstract (arXiv)

Evaluation of deep reinforcement learning (RL) is inherently challenging. In particular, learned policies are largely opaque, and hypotheses about the behavior of deep RL agents are difficult to test in black-box environments. Considerable effort has gone into addressing opacity, but almost no effort has been devoted to producing high quality environments for experimental evaluation of agent behavior. We present TOYBOX, a new high-performance, open-source* subset of Atari environments re-designed for the experimental evaluation of deep RL. We show that TOYBOX enables a wide range of experiments and analyses that are impossible in other environments. *https://kdl-umass.github.io/Toybox/