期刊
IEEE TRANSACTIONS ON INFORMATION THEORY
卷 59, 期 4, 页码 1966-1980出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2012.2234824
关键词
Causal entropy; correlated equilibrium (CE); directed information; inverse optimal control; inverse reinforcement learning; maximum entropy; statistical estimation
资金
- Richard King Mellon Foundation
- Quality of Life Technology Center
- Office of Naval Research Reasoning in Reduced Information Spaces project MURI
The principle of maximum entropy provides a powerful framework for estimating joint, conditional, and marginal probability distributions. However, there are many important distributions with elements of interaction and feedback where its applicability has not been established. This paper presents the principle of maximum causal entropy-an approach based on directed information theory for estimating an unknown process based on its interactions with a known process. We demonstrate the breadth of the approach using two applications: a predictive solution for inverse optimal control in decision processes and computing equilibrium strategies in sequential games.
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