A survey of progress on cooperative multi-agent reinforcement learning in open environment
Attention-guided contrastive role representations for multi-agent reinforcement learning
Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
Coordinating Multi-Agent Reinforcement Learning via Dual Collaborative Constraints
Many real-world multi-agent tasks exhibit a nearly decomposable structure, where
interactions among agents within the same interaction set are strong while interactions …
interactions among agents within the same interaction set are strong while interactions …
Self-motivated multi-agent exploration
In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve
a balance between self-exploration and team collaboration. However, agents can hardly …
a balance between self-exploration and team collaboration. However, agents can hardly …
MaDE: Multi-Scale Decision Enhancement for Multi-Agent Reinforcement Learning
In the domain of multi-agent reinforcement learning (MARL), the limited information
availability, complex agent interactions, and individual capabilities among agents often pose …
availability, complex agent interactions, and individual capabilities among agents often pose …
Robust cooperative multi-agent reinforcement learning via multi-view message certification
Many multi-agent scenarios require message sharing among agents to promote
coordination, hastening the robustness of multi-agent communication when policies are …
coordination, hastening the robustness of multi-agent communication when policies are …