AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging

• URL: http://arxiv.org/abs/2408.03624v1
• Date: Wed, 7 Aug 2024 08:34:48 GMT
• Title: AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
• Authors: Senkang Hu, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang, Sam Kwong,
• Abstract summary: Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. We propose a novel collaborative decision-making framework, named AgentsCoMerge, to leverage large language models (LLMs)
• Score: 46.69777653051523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue and enhance the safety and efficiency of connected and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel collaborative decision-making framework, named AgentsCoMerge, to leverage large language models (LLMs). Specifically, we first design a scene observation and understanding module to allow an agent to capture the traffic environment. Then we propose a hierarchical planning module to enable the agent to make decisions and plan trajectories based on the observation and the agent's own state. In addition, in order to facilitate collaboration among multiple agents, we introduce a communication module to enable the surrounding agents to exchange necessary information and coordinate their actions. Finally, we develop a reinforcement reflection guided training paradigm to further enhance the decision-making capability of the framework. Extensive experiments are conducted to evaluate the performance of our proposed method, demonstrating its superior efficiency and effectiveness for multi-agent collaborative decision-making under various ramp merging scenarios.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic [11.682456863110767]
  CoMAL is a framework designed to address the mixed-autonomy traffic problem by collaboration among autonomous vehicles to optimize traffic flow. CoMAL is built upon large language models, operating in an interactive traffic simulation environment.
  arXiv  Detail & Related papers  (2024-10-18T10:53:44Z)
• Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots [1.1049608786515839]
  This paper presents the Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework. CatMiP uses multi-agent reinforcement learning to coordinate agents with heterogeneous sensing, motion, and actuation capabilities. It easily adapts to mission complexities and communication constraints, and scales to varying environment sizes and team compositions.
  arXiv  Detail & Related papers  (2024-10-08T21:14:09Z)
• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models [15.951550445568605]
  Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. We propose the KoMA framework consisting of multi-agent interaction, multi-step planning, shared-memory, and ranking-based reflection modules.
  arXiv  Detail & Related papers  (2024-07-19T12:13:08Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach [28.477463632107558]
  We develop a modular framework called LLaMAC to address hallucination in Large Language Models and coordination in Multi-Agent Systems. LLaMAC implements a value distribution encoding similar to that found in the human brain, utilizing internal and external feedback mechanisms to facilitate collaboration and iterative reasoning among its modules.
  arXiv  Detail & Related papers  (2023-11-23T10:14:58Z)
• Research on Multi-Agent Communication and Collaborative Decision-Making Based on Deep Reinforcement Learning [0.0]
  This thesis studies the cooperative decision-making of multi-agent based on the Multi-Agent Proximal Policy Optimization algorithm. Different agents can alleviate the non-stationarity caused by local observations through information exchange between agents. The experimental results show that the improvement has achieved certain effects, which can better alleviate the non-stationarity of the multi-agent environment.
  arXiv  Detail & Related papers  (2023-05-23T14:20:14Z)
• Multi-agent Deep Covering Skill Discovery [50.812414209206054]
  We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
  arXiv  Detail & Related papers  (2022-10-07T00:40:59Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.