Data to 7 Oct 2026

Coordinated Multi-Agent Imitation Learning

arXiv 1703.03121 ↗First posted 9 Mar 2017cs.LGcited by 70

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Cited by 23 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2018: 1; 2019: 4; 2020: 1; 2021: 13; 2022: 13; 2023: 15; 2024: 8; 2025: 10; 2026: 5.

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2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
2019State Alignment-based Imitation Learningcited by 73
2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
2021Towards Distraction-Robust Active Visual Trackingcited by 30
2022Evaluation of creating scoring opportunities for teammates in soccer via trajectory predictioncited by 19
2024MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scalecited by 17
2021Object-Aware Regularization for Addressing Causal Confusion in Imitation Learningcited by 16
2021baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modelingcited by 14
2022Interaction Modeling with Multiplex Attentioncited by 14
2023Diverse Conventions for Human-AI Collaborationcited by 14
2021Multi-Agent Imitation Learning with Copulascited by 13
2022Conditional Imitation Learning for Multi-Agent Gamescited by 13

38 more

2022Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learningcited by 13
2022Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RLcited by 11
2024Multi-Agent Imitation Learning: Value is Easy, Regret is Hardcited by 11
2023Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observationscited by 9
2023Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learningcited by 9
2023Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrationscited by 8
2024FootBots: A Transformer-based Architecture for Motion Prediction in Soccercited by 8
2021Time-series Imputation of Temporally-occluded Multiagent Trajectoriescited by 7
2023Automated Task-Time Interventions to Improve Teamwork using Imitation Learningcited by 7
2025OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccercited by 7
2020Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Criticcited by 6
2021Data-driven Analysis for Understanding Team Sports Behaviorscited by 6
2021MARL with General Utilities via Decentralized Shadow Reward Actor-Criticcited by 5
2021Learning interaction rules from multi-animal trajectories via augmented behavioral modelscited by 5
2021How does AI play football? An analysis of RL and real-world football strategiescited by 5
2022Learning to Transfer Role Assignment Across Team Sizescited by 5
2025Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuningcited by 5
2021The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Expertscited by 4
2021Multi-Agent Advisor Q-Learningcited by 4
2024Select to Perfect: Imitating desired behavior from large multi-agent datacited by 4
2025Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learningcited by 4
2024Enabling Multi-Robot Collaboration from Single-Human Guidancecited by 3
2025MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrationscited by 3
2019Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learningcited by 2
2022Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrationscited by 2
2022A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learningcited by 2
2022Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Controlcited by 2
2023Presenting Multiagent Challenges in Team Sports Analyticscited by 2
2023Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learningcited by 2
2023Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learningcited by 2
2023Multi-Agent Strategy Explanations for Human-Robot Collaborationcited by 2
2024Scalable Interactive Machine Learning for Future Command and Controlcited by 2
2025Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrationscited by 2
2025Imitation Learning for Satellite Attitude Control under Unknown Perturbationscited by 2
2025Rate optimal learning of equilibria from datacited by 2
2019Inverse Dynamic Games Based on Maximum Entropy Inverse Reinforcement Learningcited by 1
2021Enhancing Trajectory Prediction using Sparse Outputs: Application to Team Sportscited by 1
2021Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learningcited by 1

50 most cited of 70 All 70 (JSON)