Coordinating Multi-Agent Reinforcement Learning via Dual Collaborative Constraints.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Real-world multi-agent tasks often have a nearly decomposable structure.
• Coordinating agents in these tasks is crucial for learning efficiency in multi-agent reinforcement learning (MARL).
• Existing MARL algorithms struggle to effectively model and leverage this decomposable structure.

Purpose of the Study:

• To propose a novel algorithm, Dual Collaborative Constraints (DCC), for cooperative multi-agent tasks.
• To address the limitations of existing methods in handling nearly decomposable task structures.
• To enhance learning efficiency through improved agent coordination.

Main Methods:

• DCC identifies interaction sets as subtasks.
• It employs a bi-level structure for agent distribution into subtasks.
• Mutual information-based local and global collaborative constraints are proposed for intra- and inter-subtask coordination.

Main Results:

• DCC achieves both intra-subtask consensus and inter-subtask superior joint actions.
• Agents within subtasks reach consensus on local actions.
• The algorithm maximizes overall task performance.
• Experimental evaluations show superior performance against state-of-the-art baselines.

Conclusions:

• DCC effectively models nearly decomposable structures in multi-agent tasks.
• The algorithm enhances coordination and learning efficiency in cooperative MARL.
• DCC demonstrates significant improvements over existing methods.