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

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