Single-step Options for Adversary Driving
This addresses safety in autonomous driving for real-world applications, but appears incremental as it builds on existing planning methods.
The paper tackles safety driving in adversary settings by reusing knowledge from state-of-the-art planning methods through single-step options, resulting in easier and faster training and outperforming primitive-action agents, human testers, and the planning methods themselves.
In this paper, we use reinforcement learning for safety driving in adversary settings. In our work, the knowledge in state-of-art planning methods is reused by single-step options whose action suggestions are compared in parallel with primitive actions. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the state-of-art planning methods that our agent queries as skill options.