Research map / Multi-agent systems and games

Games and equilibria: research map

526 accepted papers on Games and equilibria in Multi-agent systems and games, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2025.

Within Multi-agent systems and games, its share shrank from 20.2% in 2023–24 to 7.5% in 2025–26 (127 → 143 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

game · player · sum · 302 papers

Approaches in this cluster:

Learning in Markov games (111 papers)
Proves convergence and hardness of decentralized policy learning in zero-sum and general-sum Markov games.
Cooperation and social dilemmas (95 papers)
Studies how learning agents cooperate or collude in dilemmas and sustain cooperation mechanisms.
Opponent modeling in competitive games (75 papers)
Shapes and adapts to opponents in two-player games via auto-curricula and strategy representations.
Mean-field game learning (21 papers)
Learns equilibria of mean-field games with reinforcement and imitation learning.

Most cited and most cited since 2024:

Mean Field Multi-Agent Reinforcement Learning (ICML 2018 · 313 citations)
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning (ICLR 2017 · 127 citations)
Approximating the Shapley Value without Marginal Contributions (AAAI 2024 · 28 citations)
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning (AAAI 2024 · 8 citations)

principal · strategic · welfare · 224 papers