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Found 5 papers, 2 papers with code
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
Traffic signal control has a great impact on alleviating traffic congestion in modern cities.
Enhance Reasoning for Large Language Models in the Game Werewolf
This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents.
Multi-Agent Sequential Decision-Making via Communication
Communication helps agents to obtain information about others so that better coordinated behavior can be learned.
Sequential Communication in Multi-Agent Reinforcement Learning
Agents determine the priority of decision-making by comparing the value of intention.
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control
To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
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