Efficient Communication via Self-Supervised Information Aggregation for Online and Offline Multiagent Reinforcement...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Effective coordination in cooperative multiagent reinforcement learning (MARL) relies on inter-agent communication.
• Prior methods often use raw messages, leading to inefficiencies in policy learning.
• Efficient message aggregation is crucial for advanced coordination in MARL.

Purpose of the Study:

• To propose a novel method, Multiagent communication via Self-supervised Information Aggregation (MASIA), for efficient message aggregation in cooperative MARL.
• To enhance policy learning by enabling agents to create and utilize compact, relevant message representations.
• To introduce the first offline benchmarks for evaluating multiagent communication.

Main Methods:

• Developed a permutation-invariant message encoder for generating aggregated message representations.
• Employed a self-supervised approach optimizing the encoder through information reconstruction and prediction.
• Introduced a message extraction mechanism for agents to select relevant aggregated information for decision-making.

Main Results:

• MASIA significantly improves coordination and policy learning in cooperative MARL settings.
• The proposed method demonstrates superior performance in both online and offline learning scenarios.
• Empirical validation confirms the effectiveness of MASIA and the utility of the new offline benchmarks.

Conclusions:

• Efficient message aggregation is essential for effective coordination in cooperative MARL.
• MASIA offers a robust framework for enhancing communication and decision-making in multiagent systems.
• The released offline benchmarks will serve as a valuable resource for future research in multiagent communication.