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It is essential for an automated vehicle in the field to performdiscretionary lane changes with appropriate roadmanship - driving safely andefficiently without annoying or endangering other road users - under a widerange of traffic cultures and driving conditions. While deep reinforcementlearning methods have excelled in recent years and been applied to automatedvehicle driving policy, there are concerns about their capability to quicklyadapt to unseen traffic with new environment dynamics. We formulate thischallenge as a multi-Markov Decision Processes (MDPs) adaptation problem anddeveloped Meta Reinforcement Learning (MRL) driving policies to showcase theirquick learning capability. Two types of distribution variation in environmentswere designed and simulated to validate the fast adaptation capability ofresulting MRL driving policies which significantly outperform a baseline RL.

Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning