Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models | HTML5

Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capac…

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtland Chua Roberto Calandra Rowan McAllister Sergey Levine Berkeley Artificial Intelligence Research University of California, Berkeley {kchua, roberto.calandra, rmcallister, svlevine}@berkeley.edu Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study

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