How can I efficiently coordinate agents with shared resources using identical strategies?
Symmetric Policy Design for Multi-Agent Dispatch Coordination in Supply Chains
April 29, 2025
https://arxiv.org/pdf/2504.19397This paper explores how multiple independent agents (e.g., warehouses) can efficiently share a limited resource (e.g., delivery trucks) without central control. It proposes a "symmetric" strategy where all agents follow the same decision rule based on their individual needs and shared observations of past actions. This approach simplifies coordination and promotes fairness.
Key points relevant to LLM-based multi-agent systems:
- Decentralized Coordination: The symmetric strategy demonstrates effective coordination without a central authority, a key principle in many multi-agent architectures.
- Common Information Approach: The use of shared history for decision-making mirrors how LLMs can leverage conversation history or shared context in multi-agent interactions.
- Dynamic Programming: While the specific algorithm might not translate directly, the idea of optimizing decisions based on expected future outcomes is relevant to planning and reinforcement learning in LLM agents.
- Fairness and Scalability: These are crucial considerations in multi-agent LLM systems, and this research offers insights into how to achieve them through symmetrical strategies.
- Potential for Complex Scenarios: The paper discusses extending this approach to more complex scenarios, suggesting its potential applicability to richer multi-agent LLM applications.