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Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learning

arXiv 1911.07503 ↗First posted 18 Nov 2019eess.SY cs.GT cs.MAcited by 1

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

We consider the inverse problem of dynamic games, where cost function parameters are sought which explain observed behavior of interacting players. Maximum entropy inverse reinforcement learning is extended to the N-player case in order to solve inverse dynamic games with continuous-valued state and control spaces. We present methods for identification of cost function parameters from observed data which correspond to (i) a Pareto efficient solution, (ii) an open-loop Nash equilibrium or (iii) a feedback Nash equilibrium. Furthermore, we give results on the unbiasedness of the estimation of cost function parameters for each arising class of inverse dynamic game. The applicability of the methods is demonstrated with simulation examples of a nonlinear and a linear-quadratic dynamic game.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 4 of its 41 references that are in the map. First posted on arXiv 18 Nov 2019.

Builds on

4 of 41 references.

References · 4

  1. 2016Cooperative Inverse Reinforcement Learningcited by 298
  2. 2017Coordinated Multi-Agent Imitation Learningcited by 70
  3. 2016Inverse Reinforcement Learning in Swarm Systemscited by 18
  4. 2014Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Gamescited by 17

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022Safety-Aware Multi-Agent Apprenticeship Learningcited by 0

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  1. 2026Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization
  2. 2022Weighted Maximum Entropy Inverse Reinforcement Learning
  3. 2026Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward
  4. 2025Cost Function Estimation Using Inverse Reinforcement Learning with Minimal Observations
  5. 2018Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
  6. 2026A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning ↗
  7. 2024On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
  8. 2025Kernel Based Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games