mlsanityData to 14 Oct 2026

Computational Rationalization: The Inverse Equilibrium Problem

arXiv 1308.3506 ↗First posted 15 Aug 2013cs.GT cs.LG stat.MLcited by 24

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Abstract (arXiv)

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 situations. In this work, we consider similar tasks in competitive and cooperative multi-agent domains. Here, unlike single-agent settings, a player cannot myopically maximize its reward; it must speculate on how the other agents may act to influence the game's outcome. Employing the game-theoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Aug 2013.

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Citing papers per year: 2018: 3; 2019: 1; 2020: 0; 2021: 5; 2022: 1; 2023: 2; 2024: 5; 2025: 4; 2026: 3.

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  1. 2018An Algorithmic Perspective on Imitation Learningcited by 459
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
  3. 2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
  4. 2021Choice Set Misspecification in Reward Inferencecited by 13
  5. 2024Multi-Agent Imitation Learning: Value is Easy, Regret is Hardcited by 11
  6. 2022Individual-Level Inverse Reinforcement Learning for Mean Field Gamescited by 10
  7. 2018Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Gamescited by 4
  8. 2021Multi-Agent Advisor Q-Learningcited by 4
  9. 2025Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learningcited by 4
  10. 2021Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Networkcited by 3
  11. 2021Using Inverse Optimization to Learn Cost Functions in Generalized Nash Gamescited by 3
  12. 2024Auto-Encoding Bayesian Inverse Gamescited by 3
12 more
  1. 2025Efficient Inverse Multiagent Learningcited by 3
  2. 2024Cooperation and Control in Delegation Gamescited by 2
  3. 2024Inverse Concave-Utility Reinforcement Learning is Inverse Game Theorycited by 2
  4. 2026Deep Incentive Design with Differentiable Equilibrium Blockscited by 2
  5. 2023Soft-Bellman Equilibrium in Affine Markov Games: Forward Solutions and Inverse Learningcited by 1
  6. 2021Operator Splitting for Learning to Predict Equilibria in Convex Gamescited by 0
  7. 2023Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learningcited by 0
  8. 2024Mean Field Correlated Imitation Learningcited by 0
  9. 2025Multi-Agent Inverse Q-Learning from Demonstrationscited by 0
  10. 2025Blind Inverse Game Theory: Jointly Decoding Rewards and Rationality in Entropy-Regularized Competitive Gamescited by 0
  11. 2026Bayesian Inverse Games with High-Dimensional Multi-Modal Observationscited by 0
  12. 2026DIML: Differentiable Inverse Mechanism Learning from Behaviors of Multi-Agent Learning Trajectoriescited by 0