Cover Image

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bokade, Rohit, Jin, Xiaoning
Format:	Preprint
Published:	2024
Subjects:	Multiagent Systems Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
by: Bokade, Rohit, et al.
Published: (2024)

Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
by: Zhang, Yuli, et al.
Published: (2025)

Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control
by: Kwesiga, Dickness Kakitahi, et al.
Published: (2024)

Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
by: Wang, Kang, et al.
Published: (2024)

Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
by: Li, Yihong, et al.
Published: (2025)

A Semi Centralized Training Decentralized Execution Architecture for Multi Agent Deep Reinforcement Learning in Traffic Signal Control
by: Rezaali, Arash, et al.
Published: (2025)

Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
by: Akman, Ahmet Onur, et al.
Published: (2025)

Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control
by: Duan, Wenchang, et al.
Published: (2024)

Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
by: Gu, Hankang, et al.
Published: (2025)

A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning
by: Gu, Hankang, et al.
Published: (2025)

Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning
by: Sur, Indranil, et al.
Published: (2024)

Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
by: Oh, Dahyun, et al.
Published: (2026)

Heterogeneity in Multi-Agent Reinforcement Learning
by: Hu, Tianyi, et al.
Published: (2025)

MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
by: Lee, Sunbowen, et al.
Published: (2024)

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System
by: Du, Haikuo, et al.
Published: (2025)

MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control
by: Zhu, Liwen, et al.
Published: (2021)

Adaptability in Multi-Agent Reinforcement Learning: A Framework and Unified Review
by: Hu, Siyi, et al.
Published: (2025)

AgentScope: A Flexible yet Robust Multi-Agent Platform
by: Gao, Dawei, et al.
Published: (2024)

Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
by: Li, Simin, et al.
Published: (2025)

Measuring Policy Distance for Multi-Agent Reinforcement Learning
by: Hu, Tianyi, et al.
Published: (2024)

Multi-Agent Reinforcement Learning with Communication-Constrained Priors
by: Yang, Guang, et al.
Published: (2025)

Causal Mean Field Multi-Agent Reinforcement Learning
by: Ma, Hao, et al.
Published: (2025)

Multi-source Plume Tracing via Multi-Agent Reinforcement Learning
by: Granadeno, Pedro Antonio Alarcon, et al.
Published: (2025)

AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning
by: Zhou, Guangchong, et al.
Published: (2024)

Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
by: Rudd-Jones, James, et al.
Published: (2025)

GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
by: Jiang, Haoyuan, et al.
Published: (2024)

Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization
by: Li, Wenwu, et al.
Published: (2026)

CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
by: Ruan, Jingqing, et al.
Published: (2024)

SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning
by: Long, Qian, et al.
Published: (2024)

Achieving Equilibrium under Utility Heterogeneity: An Agent-Attention Framework for Multi-Agent Multi-Objective Reinforcement Learning
by: Li, Zhuhui, et al.
Published: (2025)

Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
by: Liu, Qi, et al.
Published: (2024)

Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion
by: Wang, Ziyi, et al.
Published: (2025)

Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity
by: Mai, Yuxiang, et al.
Published: (2025)

FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning
by: Li, Wenzhe, et al.
Published: (2024)

Cooperation and Fairness in Multi-Agent Reinforcement Learning
by: Aloor, Jasmine Jerry, et al.
Published: (2024)

Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
by: Lichtlé, Nathan, et al.
Published: (2024)

Multicopy Reinforcement Learning Agents
by: Wolfe, Alicia P., et al.
Published: (2023)

Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning
by: Toquebiau, Maxime, et al.
Published: (2024)

Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing
by: Weil, Jannis, et al.
Published: (2024)

Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
by: Elrod, Michael, et al.
Published: (2025)