Autonomous drifting using simulation-aided reinforcement learning
ICRA, pp. 5442-5448, 2016.
We introduce a framework that combines simple and complex continuous state-action simulators with a real-world robot to efficiently find good control policies, while minimizing the number of samples needed from the physical robot. The framework combines the strengths of various simulation levels by first finding optimal policies in a simp...More
Full Text (Upload PDF)
PPT (Upload PPT)