Can GNNs improve MARL for supply chain inventory control?
Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chains
October 25, 2024
https://arxiv.org/pdf/2410.18631This paper tackles the problem of optimizing inventory control in complex supply chains using a multi-agent reinforcement learning (MARL) approach. It leverages graph neural networks (GNNs) to represent the relationships between different entities in the supply chain, allowing agents to learn collaboratively and adapt to changing conditions.
Key points for LLM-based multi-agent systems:
- Redefining action space: Instead of directly outputting order quantities, agents output parameters for a heuristic inventory policy, making the system adaptable and easing the challenge of integer-valued actions.
- Information aggregation: The paper proposes using a global mean pooling mechanism within the GNN to reduce the dimensionality of information passed to the central critic, improving scalability and potentially reducing overfitting.
- Noise injection: Adding noise to the value function acts as a regularizer, enhancing exploration and robustness in complex multi-agent environments.