How can I best combine different value decomposition methods for multi-agent RL?
Heterogeneous Value Decomposition Policy Fusion for Multi-Agent Cooperation
This paper introduces Heterogeneous Policy Fusion (HPF), a new way to train AI agents for cooperation in tasks requiring teamwork. HPF combines different existing methods (specifically Value Decomposition methods) for training individual agents within a multi-agent system, choosing the best approach adaptively based on performance. This allows the system to learn faster and more effectively than using any single existing method alone. It prevents agents from getting stuck in suboptimal behavior by encouraging them to learn from methods that find optimal joint actions.
Key points for LLM-based multi-agent systems: HPF could enhance multi-agent systems where agents need to work together effectively, especially in partially observable environments where agents only have limited information. This is relevant to LLM-based agents interacting in complex scenarios. The adaptive policy selection within HPF could be useful for dynamically selecting appropriate LLM prompting strategies or specialized LLMs within a multi-agent setup depending on the current situation. The focus on efficient training in HPF is also highly relevant to LLM-based systems, given the computational cost involved.