Research map / Multi-agent systems and games / Games and equilibria
Learning in Causal Markov Games
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Introduces causal Markov games that model unobserved confounders affecting agents' actions, rewards, and transitions, and studies learning in this setting.
Problem
Standard Markov game models in multi-agent RL ignore unobserved confounders (e.g., cognitive biases) that influence both agents' decisions and rewards/future states, which can make standard learning methods fail.
Approach
Extends Markov games with causal structure so that unobserved confounders jointly affect actions, rewards, and transitions, and develops learning approaches that account for these confounders.
Experimental setup
Builds on: Markov games
Closest papers by meaning
- Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making (ICML 2025 · similarity 0.89)
- Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games (ICML 2025 · similarity 0.88)
- Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem (NeurIPS 2024 · similarity 0.88)
- Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games (ICML 2024 · similarity 0.88)
- Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence beyond the Minty Property (AAAI 2024 · similarity 0.88)
- On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning (NeurIPS 2025 · similarity 0.88)
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