  Lane change is a crucial vehicle maneuver which needs coordination with
surrounding vehicles. Automated lane changing functions built on rule-based
models may perform well under pre-defined operating conditions, but they may be
prone to failure when unexpected situations are encountered. In our study, we
proposed a Reinforcement Learning based approach to train the vehicle agent to
learn an automated lane change behavior such that it can intelligently make a
lane change under diverse and even unforeseen scenarios. Particularly, we
treated both state space and action space as continuous, and designed a
Q-function approximator that has a closed- form greedy policy, which
contributes to the computation efficiency of our deep Q-learning algorithm.
Extensive simulations are conducted for training the algorithm, and the results
illustrate that the Reinforcement Learning based vehicle agent is capable of
learning a smooth and efficient driving policy for lane change maneuvers.
