# Computational Rationalization: The Inverse Equilibrium Problem

@inproceedings{Waugh2011ComputationalRT, title={Computational Rationalization: The Inverse Equilibrium Problem}, author={K. Waugh and Brian D. Ziebart and J. Andrew Bagnell}, booktitle={ICML}, year={2011} }

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can then be used to accurately predict or imitate future behavior in similar observed or unobserved…

## 68 Citations

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning

- Computer ScienceJournal of Artificial Intelligence Research
- 2021

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The results show that rationalizing an equilibrium is computationally easier than computing it; from a practical perspective a practitioner can use the algorithms to validate behavioral models.

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Through the learned utility functions from the fitness game, this work hopes to gain insight into the relative importance each user places on safeguarding their privacy vs. achieving the other desirable objectives in the game.

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- 2014

This paper identifies three structural conditions and shows that if the data set of observed behavior satisfies any of these conditions, then it can be explained by payoff matrices for which Nash equilibria are efficiently computable.

Cooperative Inverse Reinforcement Learning

- Computer ScienceNIPS
- 2016

It is shown that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, it is proved that optimality in isolation is suboptimal in C IRL, and an approximate CirL algorithm is derived.

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- 2019

This paper draws upon well-known ideas in decision theory to obtain a concise and interpretable agent behavior model, and derive solvers and gradients for end-to-end learning and proposes an efficient first-order primal-dual method which exploits the structure of extensive-form games.

Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

- Mathematics, Computer ScienceArXiv
- 2021

A new notion of stochastic Nash equilibrium for boundedly rational agents, which is called the Entropic Cost Equilibrium (ECE), is defined and it is shown that ECE is a natural extension to multiple agents of Maximum Entropy optimality for single agents.

Multiagent Inverse Reinforcement Learning for Two-Person Zero-Sum Games

- Computer ScienceIEEE Transactions on Games
- 2018

A theoretical foundation for competitive two-agent zero-sum MIRL problems is established and a Bayesian solution approach is proposed in which the generative model is based on an assumption that the two agents follow a minimax bipolicy.

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