# DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo

arXiv: https://arxiv.org/abs/2205.08034 · HTML: https://mlsanity.com/paper/2205.08034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 3 of 3)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [ViZDoom Competitions: Playing Doom from Pixels](https://mlsanity.com/paper/1809.03470/) (2018)
- [Unified Distributed Environment](https://mlsanity.com/paper/2205.06946/) (2022)

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

- [PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.10714/) (2022)

## Abstract (arXiv)

We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging custom tasks in ROS and Gazebo simulation environments. This toolkit provides building blocks of advanced features such as collision detection, behaviour control, domain randomization, spawner, and many more. DeepSim is designed to reduce the boundary between robotics and machine learning communities by providing Python interface. In this paper, we discuss the components and design decisions of DeepSim Toolkit.