  With the implementation of reinforcement learning (RL) algorithms, current
state-of-art autonomous vehicle technology have the potential to get closer to
full automation. However, most of the applications have been limited to game
domains or discrete action space which are far from the real world driving.
Moreover, it is very tough to tune the parameters of reward mechanism since the
driving styles vary a lot among the different users. For instance, an
aggressive driver may prefer driving with high acceleration whereas some
conservative drivers prefer a safer driving style. Therefore, we propose an
apprenticeship learning in combination with deep reinforcement learning
approach that allows the agent to learn the driving and stopping behaviors with
continuous actions. We use gradient inverse reinforcement learning (GIRL)
algorithm to recover the unknown reward function and employ REINFORCE as well
as Deep Deterministic Policy Gradient algorithm (DDPG) to learn the optimal
policy. The performance of our method is evaluated in simulation-based scenario
and the results demonstrate that the agent performs human like driving and even
better in some aspects after training.
