Reinforcement Learning

Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach

The significant advancements in large language models (LLMs) have presented novel opportunities for tackling planning and decision-making within multi-agent systems. However, as the number of agents increases, the issues of hallucination in LLMs and …

A General Scenario-agnostic Reinforcement Learning for Traffic Signal Control

Reinforcement learning has been recently adopted to revolutionize and optimize traditional traffic signal control systems. Existing methods are either based on a single scenario or multiple independent scenarios, where each scenario has a separate …