Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning

• URL: http://arxiv.org/abs/2308.13491v1
• Date: Fri, 25 Aug 2023 17:05:41 GMT
• Title: Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning
• Authors: Dvij Kalaria, Qin Lin and John M. Dolan
• Abstract summary: We propose a head-to-head racing environment for reinforcement learning which accurately models vehicle dynamics. We also propose a control barrier function-based safe reinforcement learning algorithm to enforce the safety of the agent.
• Score: 22.69532642800264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also actively looking for strategies to overtake/stay ahead of the opponent. In this work we propose a head-to-head racing environment for reinforcement learning which accurately models vehicle dynamics. Some previous works have tried learning a policy directly in the complex vehicle dynamics environment but have failed to learn an optimal policy. In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy. We also propose a control barrier function-based safe reinforcement learning algorithm to enforce the safety of the agent in a more effective way while not compromising on optimality.

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