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Maximum Causal Tsallis Entropy Imitation Learning
[article]
2018
arXiv
pre-print
In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from demonstrations. We provide the full mathematical analysis of the proposed framework. First, the optimal solution of an MCTE problem is shown to be a sparsemax distribution, whose supporting set can be adjusted. The proposed method has advantages over a softmax distribution in that it can exclude unnecessary actions by
arXiv:1805.08336v2
fatcat:gyqyph2subbinj5wjagvaoq4f4