Data to 7 Oct 2026

Learning Policy Representations in Multiagent Systems

arXiv 1806.06464 ↗First posted 17 Jun 2018cs.MA cs.AI cs.LGcited by 48

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Abstract (arXiv)

Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging high-dimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 11; 2023: 10; 2024: 4; 2025: 8; 2026: 4.

48 papers.

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2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 57
2021Deep Interactive Bayesian Reinforcement Learning via Meta-Learningcited by 29
2021Influencing Towards Stable Multi-Agent Interactionscited by 24
2024Learning to Cooperate with Humans using Generative Agentscited by 23
2021Model-Based Opponent Modelingcited by 21
2022Influencing Long-Term Behavior in Multiagent Reinforcement Learningcited by 18
2021Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Trainingcited by 16
2021Reinforcement Learning Under Algorithmic Triagecited by 15
2022It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generationcited by 14
2022Multi-Agent Policy Transfer via Task Relationship Modelingcited by 14
2022Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradientscited by 12
2023System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learningcited by 12

36 more

2023Towards More Human-like AI Communication: A Review of Emergent Communication Researchcited by 12
2021Learning Altruistic Behaviours in Reinforcement Learning without External Rewardscited by 8
2025Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperationcited by 8
2022PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representationscited by 6
2023Learning Models of Adversarial Agent Behavior under Partial Observabilitycited by 5
2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learningcited by 4
2024Measuring Policy Distance for Multi-Agent Reinforcement Learningcited by 4
2025Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)cited by 4
2023CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learningcited by 3
2023Hierarchical Imitation Learning for Stochastic Environmentscited by 3
2025Adaptively Coordinating with Novel Partners via Learned Latent Strategiescited by 3
2021Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learningcited by 2
2022PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learningcited by 2
2022Offline Equilibrium Findingcited by 2
2022Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processescited by 2
2022Decision-making with Speculative Opponent Modelscited by 2
2023Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environmentcited by 2
2023Diffusion Models for Multi-target Adversarial Trackingcited by 2
2023Contrastive learning-based agent modeling for deep reinforcement learningcited by 2
2024SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learningcited by 2
2025Trajectory-Class-Aware Multi-Agent Reinforcement Learningcited by 2
2021Metric Policy Representations for Opponent Modelingcited by 1
2021Partner-Aware Algorithms in Decentralized Cooperative Bandit Teamscited by 1
2022Safe adaptation in multiagent competitioncited by 1
2022Some performance considerations when using multi-armed bandit algorithms in the presence of missing datacited by 1
2024Learning Strategy Representation for Imitation Learning in Multi-Agent Gamescited by 1
2025Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observabilitycited by 1
2025Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaborationcited by 1
2025TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encodingcited by 1
2026Learning Policy Representations for Steerable Behavior Synthesiscited by 1
2021Learning to Assist Agents by Observing Themcited by 0
2023Policy Dispersion in Non-Markovian Environmentcited by 0
2025Policy Search, Retrieval, and Composition via Task Similarity in Collaborative Agentic Systemscited by 0
2026Generalized Intention Modeling in Multi-Agent Reinforcement Learningcited by 0
2026Implicit Neural Representations of Individual Behaviorcited by 0
2026Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Gamescited by 0

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