COMAD discovers and reuses coordination skills from mixed offline MARL data via auto-encoders and density-based estimation to achieve continual learning with better transfer.
A survey of progress on cooperative multi-agent reinforcement learning in open environment
9 Pith papers cite this work. Polarity classification is still indexing.
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EDV decouples execution, distillation by a third-party agent, and consensus verification to filter erroneous trajectories in LLM agent experience learning, outperforming baselines on tau2-bench, Mind2Web, and MMTB.
Conquer is a retrieve-adapt-update semantic skill-library framework with a SAG backbone for continual multi-quadruped coordination, reporting 95.6% average success in simulations and real-world validation.
PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.
Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.
Marope applies hierarchical MARL with decentralized lower-level rope policies and a centralized scheduler to achieve cooperative long rope skipping on Unitree G1 humanoids in simulation and reality.
CRONA is a MARL framework that uses modality-specialized agents with auxiliary beliefs and a centralized multi-modal critic to achieve better performance and efficiency than single-agent baselines on visual-acoustic navigation tasks.
A shared consensus vector, generated before any action, lets cooperative agents act simultaneously and lets the whole joint policy be trained with single-agent PPO.
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
citing papers explorer
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Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
COMAD discovers and reuses coordination skills from mixed offline MARL data via auto-encoders and density-based estimation to achieve continual learning with better transfer.
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Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
EDV decouples execution, distillation by a third-party agent, and consensus verification to filter erroneous trajectories in LLM agent experience learning, outperforming baselines on tau2-bench, Mind2Web, and MMTB.
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Continual Quadruped Robots Coordination via Semantic Skill Discovery
Conquer is a retrieve-adapt-update semantic skill-library framework with a SAG backbone for continual multi-quadruped coordination, reporting 95.6% average success in simulations and real-world validation.
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PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.
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Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
Aco2 trains a quadrotor policy in simulation that adapts to diverse payload dynamics via latent context encoding and contrastive structuring, enabling zero-shot real-world deployment for autonomous aerial delivery.
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Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning
Marope applies hierarchical MARL with decentralized lower-level rope policies and a centralized scheduler to achieve cooperative long rope skipping on Unitree G1 humanoids in simulation and reality.
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Cross-Modal Navigation with Multi-Agent Reinforcement Learning
CRONA is a MARL framework that uses modality-specialized agents with auxiliary beliefs and a centralized multi-modal critic to achieve better performance and efficiency than single-agent baselines on visual-acoustic navigation tasks.
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Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
A shared consensus vector, generated before any action, lets cooperative agents act simultaneously and lets the whole joint policy be trained with single-agent PPO.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.