Nonparametric Bayesian Policy Priors for Reinforcement Learning

NeurIPS 2010 Finale Doshi-VelezDavid WingateNicholas RoyJoshua B. Tenenbaum

We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations. Our nonparametric Bayesian approach combines model knowledge, inferred from expert information and independent exploration, with policy knowledge inferred from expert trajectories... (read more)

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