A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning

Background:

• Current multiagent reinforcement learning (MARL) algorithms often inherit single-agent exploration strategies.
• This inheritance leads to collaboration failures due to "responsibility diffusion" (RD), where agents avoid minority roles.

Purpose of the Study:

• To theoretically analyze the cause of RD in MARL, linking it to the exploration-exploitation dilemma.
• To propose a novel approach, policy resonance (PR), to mitigate RD and enhance collaborative exploration in MARL.

Main Methods:

• Theoretical analysis of the exploration-exploitation dilemma in large-scale MARL systems.
• Development of the policy resonance (PR) approach to refactor joint agent policies while maintaining individual policies.
• Empirical evaluation of PR integrated with state-of-the-art MARL algorithms.

Main Results:

• Demonstration that the proposed policy resonance (PR) approach effectively addresses the responsibility diffusion (RD) problem.
• Significant improvements in collaborative performance of agents in complex cooperative tasks when using PR.
• Validation of PR's effectiveness across multiple benchmark tasks.

Conclusions:

• The policy resonance (PR) approach offers a viable solution to enhance collaboration in multiagent reinforcement learning.
• PR successfully modifies collaborative exploration strategies, overcoming limitations of single-agent inheritance.
• This work provides a pathway to more robust and effective MARL systems for complex cooperative tasks.