Publication Details

SELECT * FROM publications WHERE Record_Number=10511
Reference TypeConference Proceedings
Author(s)Deisenroth, M.P.; Rasmussen, C.E.
TitlePILCO: A Model-Based and Data-Efficient Approach to Policy Search
Journal/Conference/Book TitleInternational Conference on Machine Learning (ICML 2011)
KeywordsGaussian process, reinforcement learning, policy search
AbstractIn this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.
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