mlsanityData to 7 Oct 2026

Efficient Inverse Multiagent Learning

arXiv 2502.14160 ↗First posted 20 Feb 2025cs.GT cs.AI cs.LGcited by 3

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Abstract (arXiv)

In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which we develop polynomial-time algorithms to solve, the former of which relies on an exact first-order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 85 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

19 of 85 references.

References · 19

  1. 2016Cooperative Inverse Reinforcement Learningcited by 298
  2. 2019On Gradient Descent Ascent for Nonconvex-Concave Minimax Problemscited by 266
  3. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  4. 2019On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shiftcited by 174
  5. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  6. 2019Global Optimality Guarantees For Policy Gradient Methodscited by 146
  7. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  8. 2021Independent Policy Gradient Methods for Competitive Reinforcement Learningcited by 97
  9. 2022Do Differentiable Simulators Give Better Policy Gradients?cited by 83
  10. 2021Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gapcited by 80
  11. 2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
  12. 2021Inverse Optimization: Theory and Applicationscited by 48
7 more
  1. 2022Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalitiescited by 33
  2. 2013Computational Rationalization: The Inverse Equilibrium Problemcited by 24
  3. 2015Econometrics for Learning Agentscited by 23
  4. 2014Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Gamescited by 17
  5. 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Gamescited by 9
  6. 2019Robust Multi-agent Counterfactual Predictioncited by 6
  7. 2017Inference on Auctions with Weak Assumptions on Informationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attentioncited by 1
  2. 2026DIML: Differentiable Inverse Mechanism Learning from Behaviors of Multi-Agent Learning Trajectoriescited by 0
  3. 2026Learning Incentive Structures for Cooperative Resilience in Multi-Agent Systems under Social Dilemmascited by 0