Can one actor personalize policies for diverse intersections?
Using a single actor to output personalized policy for different intersections
This paper addresses the challenge of efficiently controlling traffic signals in large-scale road networks using multi-agent reinforcement learning (MARL). Existing methods often rely on shared parameters across all intersections (agents), which can hinder performance when traffic patterns vary significantly between intersections. The proposed HAMH-PPO (Hyper-Action Multi-Head Proximal Policy Optimization) model aims to personalize policies for each intersection while maintaining efficient parameter sharing. It employs a centralized critic that estimates multiple value functions for each intersection and a hyper-action mechanism in the actor network to combine these values based on intersection-specific preferences.
Key points for LLM-based multi-agent systems:
- Personalized policies with shared parameters: HAMH-PPO addresses the trade-off between personalization and efficiency in multi-agent systems, which is relevant for LLM agents that need to adapt to individual tasks while sharing core knowledge.
- Multi-head value estimation: The use of multiple value functions provides a richer representation of potential outcomes for each agent, analogous to LLMs considering multiple perspectives or generating diverse responses.
- Hyper-action mechanism: This mechanism allows agents to dynamically weight different value functions, similar to how LLMs can attend to different parts of their input or knowledge base.
- Centralized training with decentralized execution (CTDE): This paradigm, common in MARL, offers potential benefits for LLM-based agents, enabling efficient learning through shared experience while allowing for independent action.
- Scalability: HAMH-PPO demonstrates improved performance in large-scale scenarios, highlighting its potential applicability to complex multi-agent systems with numerous LLM agents.