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Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle(AGV) navigation utilizing raw sensor data without a-priori maps or GPS, whichis a necessity in hazardous, information poor environments such as regionswhere natural disasters occur, and extraterrestrial planets. The substantialtraining time required to learn an optimal DRL policy, which can be days orweeks for complex tasks, is a major hurdle to real-world implementation in AGVapplications. Training entails repeated collisions with the surroundingenvironment over an extended time period, dependent on the complexity of thetask, to reinforce positive exploratory, application specific behavior that isexpensive, and time consuming in the real-world. Effectively bridging thesimulation to real-world gap is a requisite for successful implementation ofDRL in complex AGV applications, enabling learning of cost-effective policies.We present AutoVRL, an open-source high fidelity simulator built upon theBullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch totrain AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped withsensor implementations of GPS, IMU, LiDAR and camera, actuators for AGVcontrol, and realistic environments, with extensibility for new environmentsand AGV models. The simulator provides access to state-of-the-art DRLalgorithms, utilizing a python interface for simple algorithm and environmentcustomization, and simulation execution.

AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning