Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
This work addresses the problem of adapting autonomous driving to individual user preferences, though it is incremental as it builds on existing methods like GIRL and DDPG.
The authors tackled the challenge of autonomous driving with continuous actions and varying driving styles by combining apprenticeship learning with deep reinforcement learning, resulting in an agent that performs human-like driving and even better in some aspects after training.
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.