# MicroRacer: a didactic environment for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2203.10494 · HTML: https://mlsanity.com/paper/2203.10494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors](https://mlsanity.com/paper/2001.02811/) (2020)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning](https://mlsanity.com/paper/1911.01562/) (2019)
- [Safe Autonomous Racing via Approximate Reachability on Ego-vision](https://mlsanity.com/paper/2110.07699/) (2021)
- [Learning the Subsystem of Local Planning for Autonomous Racing](https://mlsanity.com/paper/2102.11042/) (2021)
- [Reward Signal Design for Autonomous Racing](https://mlsanity.com/paper/2103.10098/) (2021)
- [Crawling in Rogue's dungeons with (partitioned) A3C](https://mlsanity.com/paper/1804.08685/) (2018)
- [Challenging On Car Racing Problem from OpenAI gym](https://mlsanity.com/paper/1911.04868/) (2019)

## Abstract (arXiv)

MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or the need of exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.