One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors

23 Sep 2015Justin FuSergey LevinePieter Abbeel

One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy... (read more)

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