DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning
Abstract
DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub: https://git.io/fjxoJ.
Cite
@article{arxiv.1911.01562,
title = {DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning},
author = {Bharathan Balaji and Sunil Mallya and Sahika Genc and Saurabh Gupta and Leo Dirac and Vineet Khare and Gourav Roy and Tao Sun and Yunzhe Tao and Brian Townsend and Eddie Calleja and Sunil Muralidhara and Dhanasekar Karuppasamy},
journal= {arXiv preprint arXiv:1911.01562},
year = {2019}
}