  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.
