A semi-independent policies training method with shared representation for heterogeneous multi-agents reinforcement...

Area of Science:

• Artificial Intelligence
• Neuroscience
• Machine Learning

Background:

• Cooperative multi-reinforcement learning (RL) excels with homogeneous agents via parameter sharing.
• Heterogeneous agents pose challenges for parameter sharing due to diverse inputs, outputs, and objectives.
• Neuroscience reveals brain mechanisms for sharing both similar experiences and abstract concepts.

Purpose of the Study:

• To propose a semi-independent training policy for heterogeneous agents in cooperative multi-reinforcement learning.
• To address the conflict between parameter sharing and specialized training in diverse agent settings.
• To leverage brain-inspired knowledge-sharing mechanisms for improved RL performance.

Main Methods:

• A semi-independent training policy integrating shared common representations for observations and actions.
• Utilizing a shared latent space to balance upstream policy and downstream agent functions.
• Implementing a method inspired by hierarchical knowledge-sharing in the human brain.

Main Results:

• The proposed method demonstrates superior performance compared to mainstream algorithms for heterogeneous agents.
• Empirical evidence supports the method's generalizability for curriculum learning and representation transfer.
• The approach effectively tackles the challenges of diverse agent inputs, outputs, and objectives.

Conclusions:

• The novel semi-independent training policy offers a robust solution for heterogeneous multi-agent reinforcement learning.
• The brain-inspired approach facilitates effective knowledge and experience sharing among diverse agents.
• This framework provides a foundation for advanced heterogeneous agent RL, including curriculum learning and representation transfer.