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Methodology
← Methods
Reinforcement Learning
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Methods
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Multi-Agent Systems
948 directly classified papers
Papers per year
2004: 1
2005: 1
2006: 1
2007: 2
2008: 3
2009: 3
2011: 3
2012: 2
2013: 9
2014: 3
2015: 4
2016: 5
2017: 21
2018: 30
2019: 66
2020: 101
2021: 110
2022: 135
2023: 183
2024: 138
2025: 83
2026: 44
Papers
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
JMLR 2024
Learning to Cooperate with Humans using Generative Agents
NIPS 2024
TEAMSTER: Model-Based Reinforcement Learning for Ad Hoc Teamwork (Abstract Reprint)
AAAI 2024
Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos
AAAI 2024
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)
JMLR 2024
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
NIPS 2024
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
CVPR 2024
Multi-Agent Domain Calibration with a Handful of Offline Data
NIPS 2024
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning
AAAI 2024
Solving Zero-Sum Markov Games with Continuous State via Spectral Dynamic Embedding
NIPS 2024
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
NIPS 2024
Sustainability of Data Center Digital Twins with Reinforcement Learning
AAAI 2024
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
AAAI 2024
Differentially Private Reinforcement Learning with Self-Play
NIPS 2024
Finite-Time Convergence Rates of Decentralized Local Markovian Stochastic Approximation
IJCAI 2024
Factored Online Planning in Many-Agent POMDPs
AAAI 2024
Principal-Agent Reward Shaping in MDPs
AAAI 2024
Expressive Multi-Agent Communication via Identity-Aware Learning
AAAI 2024
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
AAAI 2024
Optimistic Value Instructors for Cooperative Multi-Agent Reinforcement Learning
AAAI 2024
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
AAAI 2024
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
NIPS 2024
Learning Diverse Risk Preferences in Population-Based Self-Play
AAAI 2024
Collaborative Planar Pushing of Polytopic Objects with Multiple Robots in Complex Scenes
RSS 2024
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
NIPS 2024
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