  It is essential for an automated vehicle in the field to perform
discretionary lane changes with appropriate roadmanship - driving safely and
efficiently without annoying or endangering other road users - under a wide
range of traffic cultures and driving conditions. While deep reinforcement
learning methods have excelled in recent years and been applied to automated
vehicle driving policy, there are concerns about their capability to quickly
adapt to unseen traffic with new environment dynamics. We formulate this
challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and
developed Meta Reinforcement Learning (MRL) driving policies to showcase their
quick learning capability. Two types of distribution variation in environments
were designed and simulated to validate the fast adaptation capability of
resulting MRL driving policies which significantly outperform a baseline RL.
