Nonparametric Bayesian Policy Priors for Reinforcement Learning

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Finale Doshi-velez, David Wingate, Nicholas Roy, Joshua 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. We introduce priors that bias the agent towards models with both simple representations and simple policies, resulting in improved policy and model learning.