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Efficient Model-Free Reinforcement Learning Using Gaussian Process

By Ying Fan and others
Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP, we propose Gaussian Process Posterior Sampling Reinforcement Learning(GPPSTD) algorithm in continuous state space, giving theoretical justifications and empirical results. We also provide theoretical and empirical results... Show more
December 11, 2018
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Efficient Model-Free Reinforcement Learning Using Gaussian Process
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