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Reinforcement learning by reward-weighted regression for operational space control

Jan Peters; Stefan Schaal
In: Zoubin Ghahramani (Hrsg.). Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007). International Conference on Machine Learning (ICML-2007), June 20-24, Corvallis, Oregon, USA, Pages 745-750, ACM International Conference Proceeding Series, Vol. 227, ACM, 2007.


Many robot control problems of practical importance, including operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots.

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