Hybrid Reinforcement Learning with Expert State Sequences

AAAI, 2019.

Cited by: 5|Bibtex|Views36
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are unobs...More

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