Can offline MARL handle diverse traffic control data?
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
This paper introduces OffLight, a new system for controlling traffic signals using offline multi-agent reinforcement learning (MARL). Existing MARL systems struggle to learn from real-world traffic data because it contains a mix of different control strategies (heterogeneous policies), making it hard for the AI to identify optimal actions. OffLight addresses this by using a specialized neural network (GMM-VGAE) to model the different policies present in the data and then uses techniques like importance sampling (IS) and return-based prioritized sampling (RBPS) to learn effectively from the mixed data.
For LLM-based multi-agent systems, OffLight's approach of modeling heterogeneous data could be crucial. Imagine multiple LLMs with different training or prompting styles interacting – OffLight's ability to disentangle diverse policies could help manage these interactions, especially when learning from offline interaction logs. Similarly, the importance sampling techniques could be adapted to prioritize more relevant or higher-quality interactions within a large dataset of LLM conversations. This would allow developers to train multi-agent LLM systems more effectively from diverse and potentially noisy interaction data.