# S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

arXiv: https://arxiv.org/abs/1809.09369 · HTML: https://mlsanity.com/paper/1809.09369/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [Acquisition of Chess Knowledge in AlphaZero](https://mlsanity.com/paper/2111.09259/) (2021)
- [Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero](https://mlsanity.com/paper/2310.16410/) (2023)
- [Unsupervised Representation Learning in Deep Reinforcement Learning: A Review](https://mlsanity.com/paper/2208.14226/) (2022)
- [DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization](https://mlsanity.com/paper/2207.05631/) (2022)
- [State Representation Learning from Demonstration](https://mlsanity.com/paper/1910.01738/) (2019)
- [CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2301.13473/) (2023)

## Abstract (arXiv)

State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in applications and methods makes the field lack standard evaluation datasets, metrics and tasks. This paper provides a set of environments, data generators, robotic control tasks, metrics and tools to facilitate iterative state representation learning and evaluation in reinforcement learning settings.