How can XAI simplify MADRL for V2X resource allocation?
Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
January 24, 2025
https://arxiv.org/pdf/2501.13552This paper proposes an explainable AI (XAI) method to simplify deep reinforcement learning (DRL) models for resource allocation in vehicle-to-everything (V2X) communication. The goal is to maximize data rates for vehicle-to-network (V2N) and vehicle-to-vehicle (V2V) links while ensuring reliability, particularly for latency-sensitive V2V safety messages.
Key points for LLM-based multi-agent systems:
- Explainability and Simplification: Shapley Additive Explanations (SHAP) are used to identify and remove less important input features, simplifying the DRL model and speeding up training with minimal performance loss. This is relevant to LLM agents as input complexity can be a bottleneck.
- Multi-Agent Cooperation: The system uses a multi-agent DRL approach where each V2V transmitter acts as an agent, but they are trained centrally with a shared reward to encourage cooperation. This structure could be adapted for LLM agents collaborating on a shared task.
- Decentralized Execution: While training is centralized, the agents execute their learned policies independently, relevant to distributed LLM agent deployments.
- Dynamic Environments: The paper addresses challenges related to dynamic vehicular networks, such as changing channel conditions and varying numbers of agents, which are analogous to dynamic contexts faced by LLM agents.
- Post-Hoc Explainability: The XAI method is applied after the agents are trained, offering insights into decision-making. This post-hoc analysis could be valuable for understanding LLM agent behavior.
- Simulation and Real-World Gap: The paper highlights potential challenges in translating simulation results to real-world V2X deployments. Similar considerations apply to deploying LLM agents in real-world applications. The paper suggests using "digital twins" for offline training and dynamic updates. This resonates with the use of simulated environments for bootstrapping and continuous improvement of LLM agents.