Fully Decentralized Multiagent Communication via Causal Inference.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Multiagent Systems

Background:

• Many real-world problems are modeled as multiagent (MA) reinforcement learning (RL) tasks.
• Current MA RL algorithms typically use a centralized learning with decentralized execution framework.
• Centralized learning is often impractical due to privacy concerns and the need for agents to share local information.

Purpose of the Study:

• To propose a novel approach for fully decentralized multiagent reinforcement learning.
• To enable effective communication among agents without compromising local information privacy.
• To leverage causality analysis for improved decentralized learning.

Main Methods:

• Developed a novel decentralized learning approach for multiagent reinforcement learning.
• Utilized communication among multiple agents via reinforcement learning.
• Employed causality analysis to determine the most influential counterfactuals for communication.

Main Results:

• The proposed method facilitates fully decentralized learning in multiagent systems.
• Agents can communicate effectively by selecting counterfactuals that significantly influence others.
• The approach is applicable to both classic and complex multiagent scenarios.
• Demonstrated applicability in federated learning domains.

Conclusions:

• The novel approach achieves fully decentralized learning in multiagent reinforcement learning.
• Causality-based communication enhances agent interaction and learning efficiency.
• The method offers a privacy-preserving alternative to centralized learning in multiagent systems.
• The approach shows promise for applications in federated learning and complex MA environments.