The Principle of Maximum Causal Entropy for Estimating Interacting Processes

Brian D. Ziebart, J. Andrew (Drew) Bagnell, and Anind Dey
IEEE Transactions on Information Theory, , March, 2013


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Abstract
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 work 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.

Keywords
Maximum entropy, statistical estimation, causal entropy, directed information, inverse optimal control, inverse reinforcement learning, correlated equilibrium

Notes
Number of pages: 15

Text Reference
Brian D. Ziebart, J. Andrew (Drew) Bagnell, and Anind Dey, "The Principle of Maximum Causal Entropy for Estimating Interacting Processes," IEEE Transactions on Information Theory, , March, 2013

BibTeX Reference
@article{Ziebart_2013_7401,
   author = "Brian D. Ziebart and J. Andrew (Drew) Bagnell and Anind Dey",
   title = "The Principle of Maximum Causal Entropy for Estimating Interacting Processes",
   journal = "IEEE Transactions on Information Theory",
   month = "March",
   year = "2013",
}