Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles
This work addresses the problem of autonomous vehicle navigation in mixed driving environments, but it appears incremental as it builds on existing reinforcement learning methods for a specific domain.
The paper tackles path planning for autonomous vehicles on freeways by proposing a reinforcement learning-based driving policy that requires minimal assumptions about the environment, and it shows initial results comparing the policy against an optimal dynamic programming approach and evaluating it in realistic traffic simulations.
In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow are presented.