A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles
This work addresses path planning for autonomous vehicles, but it appears incremental as it builds on existing reinforcement learning methods without introducing major innovations.
The authors tackled autonomous vehicle path planning on freeways by proposing a reinforcement learning driving policy that requires minimal environmental assumptions, and they compared its performance against an optimal dynamic programming policy and manual driving simulations using SUMO.
This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving policy based on Reinforcement Learning. The proposed policy makes minimal or no assumptions about the environment, since no a priori knowledge about the system dynamics is required. We compare the performance of the proposed policy against an optimal policy derived via Dynamic Programming and against manual driving simulated by SUMO traffic simulator.