How can I optimize LLM agent teams using hierarchical RL?
Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
January 14, 2025
https://arxiv.org/pdf/2501.06554This paper proposes a hierarchical reinforcement learning (HRL) method for optimally grouping agents in cooperative multi-agent systems. It aims to learn both optimal groupings and individual agent policies simultaneously, using a two-level approach: a high-level policy decides agent groups, while a low-level policy dictates individual agent actions within those groups.
Key points for LLM-based multi-agent systems:
- Hierarchical RL: The two-level decision-making process (group formation and individual actions) is relevant to complex multi-agent applications where LLMs could manage both high-level strategy and low-level execution.
- Permutation Invariance: The use of permutation-invariant neural networks addresses the challenge of representing groups regardless of agent order, simplifying the input space for LLMs. This is crucial for scalability as the number of agents grows.
- Centralized Training with Decentralized Execution (CTDE): This paradigm allows for efficient training of the multi-agent system while enabling independent operation of individual agents, a common requirement for web applications.
- Option-Critic Architecture: The paper adapts the option-critic framework to manage the hierarchical decision-making, offering a potential mechanism for LLMs to learn and adapt group strategies dynamically.
- Scalability: The proposed architecture aims to address scalability issues common in traditional option-critic methods, crucial for applying these techniques to larger multi-agent web applications.