Can I reuse policies to speed up MARL traffic signal control?
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
This paper introduces PLight and PRLight, two novel algorithms for enhancing traffic signal control using multi-agent reinforcement learning (MARL) and transfer learning. PLight pre-trains agents and environment models on various traffic scenarios, while PRLight uses a similarity-based approach to select and reuse these pre-trained agents for faster adaptation to new scenarios, improving efficiency and generalization across different road networks.
Key takeaways for LLM-based multi-agent systems: The concept of pre-training agents and reusing them based on scenario similarity is highly relevant. This suggests LLMs could be pre-trained on specific conversational tasks and then deployed in similar contexts without extensive retraining, potentially addressing the computational expense and generalization limitations of current LLM-based multi-agent applications. The paper's emphasis on environment modeling highlights the importance of context representation in multi-agent LLM systems. Finally, the proposed similarity measure offers a potential method for evaluating the relevance of pre-trained LLM agents to new conversational tasks.