Recent Advances in Multi-Agent Reinforcement Learning (MARL)

This is a collection of recent MARL papers with their codes and talks (if available). Some highly-revelant but earlier or single-agent RL papers might be also included.

Maintainer: Yaru Niu

Email: [email protected]

Hierarchical MARL

• ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. NeurIPS 2022. [paper].
• Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning. NeurIPS 2020. [paper][talk][website]
• RODE: Learning Roles to Decompose Multi-Agent Tasks. ICLR 2021. [paper][code][talk]
• OPtions as REsponses: Grounding Behavioural Hierarchies in Multi-Agent Reinforcement Learning. ICML 2020. [paper][talk]
• MAVEN: Multi-Agent Variational Exploration. NeurIPS 2019. [paper][code]
• Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery. AAMAS 2020. [paper][code]
• Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. ICML 2021. [paper][code][talk]
• Feudal Multi-Agent Deep Reinforcement Learning for Traffic Signal Control. AAMAS 2020. [paper][relevant code]
• Feudal Multi-Agent Hierarchiesfor Cooperative Reinforcement Learning. arXiv 2019. [paper][talk]
• [not MARL] FeUdal Networks for Hierarchical Reinforcement Learning. ICML 2017. [paper][talk]
• Option-Critic in Cooperative Multi-Agent Systems. arXiv 2020 (abridged as an extended abstract in AAMAS 2020). [paper][extented abstract]
• Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction. arXiv 2019. [paper]
• [not MARL] Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. NIPS 2016. [paper][code][talk]
• Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning. arXiv 2021. [paper]
• Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module. arXiv 2022. [paper]

Role Learning

• RODE: Learning Roles to Decompose Multi-Agent Tasks. ICLR 2021. [paper][code][talk]
• ROMA: Multi-Agent Reinforcement Learning with Emergent Roles. ICML 2020. [paper][code][talk][website]

Communication-Based

• Multi-Agent Graph-Attention Communication and Teaming. AAMAS 2021 (Best Paper Award at MAIR2@ICCV'21). [paper][paper (MAIR2)][code][talk][talk (MAIR2)]
• Learning Correlated Communication Topology in Multi-Agent Reinforcement Learning. AAMAS 2021. [paper][talk]
• TarMAC: Targeted Multi-Agent Communication. ICML 2019. [paper][talk]
• Learning to Schedule Communication in Multi-agent Reinforcement Learning. ICLR 2019. [paper][code]
• Learning Multiagent Communication with Backpropagation. NIPS 2016. [paper]
• Learning Efficient Multi-agent Communication: An Information Bottleneck Approach. ICML 2020. [paper][code][talk]
• Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration. AAMAS 2023. [paper]
• Multi-Agent Incentive Communication via Decentralized Teammate Modeling. AAAI 2022. [paper][code]
• Self-Organized Group for Cooperative Multi-agent Reinforcement Learning. NeurIPS 2022. [paper][supplementary]
• Complementary Attention for Multi-Agent Reinforcement Learning. ICML 2023. [paper][code]
• Towards True Lossless Sparse Communication in Multi-Agent Systems. ICRA 2023. [paper][talk]

General Cooperative MARL

• Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning. ICML 2022. [paper][website]

Ad-Hoc Teaming

• Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. ICML 2021. [paper][code][talk]
• Multi-Agent Collaboration via Reward Attribution Decomposition. arXiv 2020. [paper][code][website]
• Online Ad Hoc Teamwork under Partial Observability. ICLR 2022. [paper]
• Open Ad Hoc Teamwork using Graph-based Policy Learning. ICML 2021. [paper][code][talk]

Multi-Task and Transfer Learning

• NerveNet: Learning Structured Policy with Graph Neural Networks. ICLR 2018. [paper][code][website][video]
• One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. ICML 2020. [paper][code][talk][website]
• Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing. NeurIPS 2021. [paper][talk]
• Multi-Agent Reinforcement Learning is A Sequence Modeling Problem (Multi-Agent Transformer). NeurIPS 2022. [paper][code][website]
• Updet: Universal Multi-Agent Reinforcement Learning via Policy Decoupling with Transformers. ICLR 2021. [paper][code]

Multi-Task and Meta-Learning

• Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module. arXiv 2022. [paper]
• Adaptable and Scalable Multi-Agent Graph-Attention Communication. Georgia Tech MS Thesis. [paper]

Interpretability

• Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis. arXiv 2022. [paper]
• MAVIPER: Learning Decision Tree Policies for Interpretable Multi-agent Reinforcement Learning. ECML-PKDD 2022. [paper]

Robotics

• A framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies. ICRA 2022. [paper][code 1][code 2][video]
• Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards. RA-L, 2022. [paper][code][video]

Safety

• Multi-Agent Constrained Policy Optimisation. arXiv 2022. [paper][code]
• Safe Multi-Agent Isaac Gym Benchmark (Safe MAIG). [code]

Survey

• An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective. arXiv 2021. [paper]
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms. arXiv 2021. [paper]
• Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges. arXiv 2023. [[paper](Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges)]

Benchmarks

• JaxMARL: Multi-Agent RL Environments in JAX. arXiv 2023. [papaer][code][blog]
• Python MARL (PyMARL) framework. [code]
• Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning (PyMARL2). arXiv 2023. [paper][code]
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (EPyMARL). NeurIPS 2021 (Track on Datasets and Benchmarks). [paper][code][talk][blog]
• FACMAC: Factored Multi-Agent Centralised Policy Gradients (introducing multi-agent Mujoco). arXiv 2020. [paper][code]
• TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems (PyMARL Transformers). AAMAS 2023. [paper][code]
• The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games. NeurIPS 2022 (Track on Datasets and Benchmarks). [paper][code (on-policy)][code (off-policy)][talk]
• Trust Region Policy Optimisation in Multiagent Reinforcement Learning. ICLR 2022. [paper][code][talk]
• MARLlib: Extending RLlib for Multi-agent Reinforcement Learning. arXiv 2022. [paper][code][website]
• Multi-Agent Constrained Policy Optimisation. arXiv 2022. [paper][code]
• MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control. NeurIPS 2022 (Track on Datasets and Benchmarks). [paper][code][webpage]
• Safe Multi-Agent Isaac Gym Benchmark (Safe MAIG). [code]
• Settling the Variance of Multi-Agent Policy Gradients. NeurIPS 2021. [paper][code]
• Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning. NeurIPS 2022. [paper][code]
• Heterogeneous-Agent Reinforcement Learning. [code]
• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning. JMLR 2023. [paper][code]
• Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world. NeurIPS 2022 (Track on Datasets and Benchmarks). [paper][code][website][talk]
• ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling. NeurIPS 2023. [paper][code][website]