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Cooperative Multiagent Learning and Exploration With Min-Max Intrinsic Motivation
IEEE Transactions on Cybernetics
|April 18, 2025
Summary
This study introduces E2M, a new multiagent reinforcement learning (MARL) exploration method. E2M enhances agent cooperation by minimizing surprise and maximizing social influence for better joint policy learning.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Robotics
Background:
- Multiagent reinforcement learning (MARL) faces challenges in coordinated exploration due to state uncertainties and observational inconsistencies.
- Effective exploration is crucial for learning beneficial policies in complex, dynamic environments.
Purpose of the Study:
- To propose a novel MARL exploration method, E2M, that addresses coordinated exploration challenges.
- To enhance the learning of joint policies among multiple agents.
Main Methods:
- Introduced Min-Max intrinsic motivation (E2M) incorporating surprise minimization and social influence maximization.
- Employed state entropy for surprise estimation using low-dimensional state representations.
- Utilized mutual information between agent behaviors to maximize social influence.
Main Results:
- E2M demonstrated effectiveness in enhancing cooperative capabilities across StarCraft II and Multiagent MuJoCo tasks.
- The method successfully encouraged agents to cope with unstable environments and interact cooperatively.
- Results show improved joint policy learning through surprise minimization and social influence.
Conclusions:
- E2M offers a robust solution for coordinated exploration in MARL.
- The proposed method effectively balances exploration and cooperation in multiagent systems.
- E2M shows significant promise for advancing MARL research and applications.
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