Posterior Sampling for Large Scale Reinforcement Learning

arXiv: Learning, Volume abs/1711.07979, 2017.

Cited by: 9|Views16
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Abstract:

Posterior sampling for reinforcement learning (PSRL) is a popular algorithm for learning to control an unknown Markov decision process (MDP). PSRL maintains a distribution over MDP parameters and in an episodic fashion samples MDP parameters, computes the optimal policy for them and executes it. A special case of PSRL is where at the end ...More

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