Model-based Reinforcement Learning with Neural Networks on Hierarchical Dynamic System
نویسندگان
چکیده
This paper describes our strategy to approach reinforcement learning in robotic domains including the use of neural networks. We summarize our recent work on model-based reinforcement learning where models of hierarchical dynamic system are learned with stochastic neural networks [Yamaguchi and Atkeson, 2016b], and actions are planned with stochastic differential dynamic programming [Yamaguchi and Atkeson, 2015]. Especially this paper clarifies why we believe our strategy works in complex robotic tasks such as pouring.
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