# MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning

arXiv: https://arxiv.org/abs/1909.07750 · HTML: https://mlsanity.com/paper/1909.07750/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning](https://mlsanity.com/paper/2410.15221/) (2024)
- [Investigating Memory in Model-Free RL with POPGym Arcade](https://mlsanity.com/paper/2503.01450/) (2025)
- [Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs](https://mlsanity.com/paper/2406.19812/) (2024)
- [One-shot World Models Using a Transformer Trained on a Synthetic Prior](https://mlsanity.com/paper/2409.14084/) (2024)
- [Issues with Measuring Task Complexity via Random Policies in Robotic Tasks](https://mlsanity.com/paper/2602.18856/) (2026)

## Abstract (arXiv)

We present MDP Playground, a testbed for Reinforcement Learning (RL) agents with dimensions of hardness that can be controlled independently to challenge agents in different ways and obtain varying degrees of hardness in toy and complex RL environments. We consider and allow control over a wide variety of dimensions, including delayed rewards, sequence lengths, reward density, stochasticity, image representations, irrelevant features, time unit, action range and more. We define a parameterised collection of fast-to-run toy environments in OpenAI Gym by varying these dimensions and propose to use these to understand agents better. We then show how to design experiments using MDP Playground to gain insights on the toy environments. We also provide wrappers that can inject many of these dimensions into any Gym environment. We experiment with these wrappers on Atari and Mujoco to allow for understanding the effects of these dimensions on environments that are more complex than the toy environments. We also compare the effect of the dimensions on the toy and complex environments. Finally, we show how to use MDP Playground to debug agents, to study the interaction of multiple dimensions and describe further use-cases.