  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 that various demonstration could lower expected
uncertainty and benefit posterior sampling exploration. In this way, we
combined the demonstration and exploration process together to achieve a more
efficient reinforcement learning.
