PyChrono and gym-chrono: A Deep Reinforcement Learning Framework Leveraging Multibody Dynamics to Control Autonomous Vehicles and Robots

Abstract

gym-chrono is a set of simulated environments for Deep Reinforcement Learning (DRL) extending OpenAI Gym (Brockman et al., Openai gym, 2016) with robotics and autonomous driving tasks. The physics of these environments is simulated thanks to Project Chrono (Tasora et al., Chrono: An open source multi-physics dynamics engine, 2016), an open-source multi-physics simulation engine capable of simulating Multibody Dynamics with constraints and smooth or non-smooth contacts. The most used Deep Learning frameworks (such as PyTorch and Tensorflow) have Python API, and thus using Python to implement DRL algorithms is the most convenient option. For this reason, a condition for the creation of these environments has been the development of PyChrono, a Python module consisting of the Python bindings to Project Chrono C++ API, to effectively interface the simulation capabilities of Project Chrono with Deep Learning frameworks.