Multi Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) focuses on developing algorithms that enable multiple independent agents to learn optimal strategies within a shared environment, often to achieve a common goal. Current research emphasizes improving sample efficiency and generalization, exploring novel architectures like equivariant graph neural networks and specialized network structures (e.g., Bottom-Up Networks), and addressing challenges posed by non-stationarity and partial observability through techniques such as auxiliary prioritization and global state inference with diffusion models. MARL's significance lies in its potential to solve complex real-world problems across diverse domains, including robotics, traffic control, and healthcare, by enabling effective coordination and collaboration among multiple agents.

Papers

September 26, 2023

Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
Eslam Eldeeb, Mohammad Shehab, Hirley Alves
Multi Agent Reinforcement Learning Internet of Thing Multi Agent Deep Reinforcement Learning UAV Swarm Age Invariant Face Recognition

September 22, 2023

September 20, 2023

September 18, 2023

Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem
Tanmay Vilas Samak, Chinmay Vilas Samak, Venkat Krovi
Autonomous Vehicle Multi Agent Reinforcement Learning Long Term Cooperation Multi Agent Deep Reinforcement Learning Multi Agent Learning Autonomous Racecar Autonomous Vehicle Platform

September 14, 2023

Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing
Hadar Szostak, Kobi Cohen
Multi Agent Reinforcement Learning Multi Agent Policy Single Agent Learning Active Hypothesis Testing

September 13, 2023

September 12, 2023

Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
Marwa Chafii, Salmane Naoumi, Reda Alami, Ebtesam Almazrouei, Mehdi Bennis, Merouane Debbah
Multi Agent Reinforcement Learning Emergent Communication Autonomous Decision Making MARL Algorithm Future Wireless Network

September 11, 2023

Dynamic Handover: Throw and Catch with Bimanual Hands
Binghao Huang, Yuanpei Chen, Tianyu Wang, Yuzhe Qin, Yaodong Yang, Nikolay Atanasov, Xiaolong Wang
Multi Agent Reinforcement Learning Real Robot Robot Handover Robot Arm Multi Fingered Hand

September 8, 2023

Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence
Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
Multi Agent Reinforcement Learning Sample Complexity Last Iterate Convergence Dynamic Game Differential Game Linear Quadratic Quadratic System

August 29, 2023

August 28, 2023

Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning
Min Hua, Bin Shuai, Quan Zhou, Jinhai Wang, Yinglong He, Hongming Xu
Reinforcement Learning Multi Agent Reinforcement Learning Recent Advance Transportation System Energy Management Connected Vehicle Eco Driving Future Transportation Multi Vehicle

August 27, 2023

MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange
Jiarong Fan, Hao Wang, Ariel Liebman
Multi Agent Reinforcement Learning Electric Vehicle Multi Agent Reinforcement Electric Vehicle Charging Vehicle 2 Vehicle Vehicle to Vehicle

August 25, 2023

August 24, 2023

An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
Amin Shojaeighadikolaei, Morteza Hashemi
High Efficiency Multi Agent Reinforcement Learning Electric Vehicle Electric Vehicle Charging Electric Vehicle Charging Station Distribution Grid Decentralized Policy Gradient

Multi Agent Reinforcement Learning

Papers

Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach

Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future

Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles

Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem

Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing

Characterizing Speed Performance of Multi-Agent Reinforcement Learning

Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems

Attention Loss Adjusted Prioritized Experience Replay

Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks

Dynamic Handover: Throw and Catch with Bimanual Hands

Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence

Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning

Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning

MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange

Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning

Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning

An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control