Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic

Alfredo Canziani
Alfredo Canziani

ICLR, Volume abs/1901.02705, 2019.

Cited by: 15|Bibtex|Views127
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the ...More

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