In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) frame- work for imitation learning which can efficiently learn a sparse multi-modal ...

May 22, 2018 · Abstract:In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently ...

In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) frame- work for imitation learning which can efficiently learn a sparse multi-modal ...

This paper develops a principle of maximum causal Tsallis entropy (MCTE) and applies it to imitation learning tasks. Overall, the paper makes a solid ...

This paper proves that an MCTE problem is equivalent to robust Bayes estimation in the sense of the Brier score, and proposes a maximum causal Tsallis ...

Dec 3, 2018 · Abstract. In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a ...

View recent discussion. Abstract: In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can ...

Third, we propose a maximum causal Tsallis entropy imitation learning (MCTEIL) algorithm with a sparse mixture density network (sparse MDN) by modeling mixture ...

Maximum causal tsallis entropy imitation learning. In Advances in Neural Information Processing Systems, 2018. [49] Kyungjae Lee, Sungjoon Choi, and ...

Maximum causal tsallis entropy imitation learning. K Lee, S Choi, S Oh. Advances in Neural Information Processing Systems 31 (2018), 4403-4413., 2018. 23, 2018.