# robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots

arXiv: https://arxiv.org/abs/2007.02753 · HTML: https://mlsanity.com/paper/2007.02753/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey of Imitation Learning Methods, Environments and Metrics](https://mlsanity.com/paper/2404.19456/) (2024)
- [Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization](https://mlsanity.com/paper/2309.14727/) (2023)
- [Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning](https://mlsanity.com/paper/2406.03276/) (2024)
- [rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks](https://mlsanity.com/paper/2102.04916/) (2021)
- [Learning Performance Graphs from Demonstrations via Task-Based Evaluations](https://mlsanity.com/paper/2204.05909/) (2022)
- [Shaped Policy Search for Evolutionary Strategies using Waypoints](https://mlsanity.com/paper/2105.14639/) (2021)
- [EAGERx: Graph-Based Framework for Sim2real Robot Learning](https://mlsanity.com/paper/2407.04328/) (2024)
- [Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling](https://mlsanity.com/paper/2309.11089/) (2023)
- [GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo](https://mlsanity.com/paper/2609.13243/) (2026)

## Abstract (arXiv)

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robo-gym. We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots: a mobile robot and a robot arm. The distributed capabilities of the framework enable several advantages like using distributed algorithms, separating the workload of simulation and training on different physical machines as well as enabling the future opportunity to train in simulation and real world at the same time. Finally we offer an overview and comparison of robo-gym with other frequently used state-of-the-art DRL frameworks.