Reinforcement Learning for RARS
نویسندگان
چکیده
This paper presents the results of reinforcement learning experiments for the Robot Auto Racing Simulator (RARS). We compare three different drivers, each taking a different approach to function approximation and reinforcement learning in this continuous-action problem. We report moderate success learning optimal paths around an oval track with simple learners, and we discuss difficulties encountered by more complex learners.
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تاریخ انتشار 2003