Multi Agent Deep Reinforcement Learning

Multi-agent deep reinforcement learning (MADRL) focuses on training multiple AI agents to collaborate or compete within a shared environment, aiming to optimize collective performance through learning. Current research emphasizes developing efficient algorithms like MADDPG and variations of Q-learning, often incorporating transformer networks for improved feature representation and handling complex interactions, and exploring different training paradigms such as centralized training with decentralized execution. This field is significant for its potential to solve complex real-world problems across diverse domains, including autonomous driving, robotics, network optimization, and resource management, by enabling more robust and adaptable systems.

Papers

March 5, 2023

Multi-vehicle Platoon Overtaking Using NoisyNet Multi-Agent Deep Q-Learning Network
Lv He
Autonomous Vehicle Multi Agent Deep Reinforcement Learning Single Agent Platoon Control Safe Overtaking

March 2, 2023

Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement
Petr Bobák, Ladislav Čmolík, Martin Čadík
Reinforcement Learning Multi Agent Deep Reinforcement Learning Labeling Technique Pseudo Labeled Image

February 24, 2023

Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning
Liang Wang, Zhuangkun Wei, Weisi Guo
Internet of Thing Sensor Data Federated Prompt Cooperation Multi Agent Deep Reinforcement Learning Encryption Method Physical Layer

February 7, 2023

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
Robert Loftin, Mustafa Mert Çelikok, Herke van Hoof, Samuel Kaski, Frans A. Oliehoek
LeArning Abstract Multi Agent Reinforcement Learning Multi Agent Deep Reinforcement Learning Stackelberg Equilibrium Differentiable Game CoMMIT Selection

February 6, 2023

January 9, 2023

Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning
Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle
Deep Reinforcement Learning Transfer Learning Multi Agent Deep Reinforcement Learning Network Slicing

December 23, 2022

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Joongheon Kim
Multi Agent Deep Reinforcement Learning Multiple Agent Cooperative Multi Agent Reinforcement Learning Multiple Unmanned Aerial Vehicle Centralized Training UAV Swarm Autonomous Mobility on Demand

December 15, 2022

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
Kaidi Xu, Nguyen Van Huynh, Geoffrey Ye Li
Multi Agent Reinforcement Learning Multi Agent Deep Reinforcement Learning Power Allocation Heterogeneous Network Power Control Distributed Training

December 14, 2022

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer
Markov Decision Process Efficient Hybrid Multi Agent Deep Reinforcement Learning Sequential Decision Making Problem Demand Prediction Autonomous Mobility Multi Agent Soft Actor Critic

November 29, 2022

Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework
Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones, Yousif Dafalla, Alexandru G. Bardas, Reza Ahmadi, Morteza Haashemi
Reinforcement Learning Deep Reinforcement Learning Multi Agent Deep Reinforcement Learning Smart Grid Demand Response Decentralized Energy

November 15, 2022

Coordination for Connected and Automated Vehicles at Non-signalized Intersections: A Value Decomposition-based Multiagent Deep Reinforcement Learning Approach
Zihan Guo, Yan Wu, Lifang Wang, Junzhi Zhang
Reinforcement Learning Proximal Policy Optimization Automated Driving Multi Agent Deep Reinforcement Learning Prior Coordination Connected Behavior Mixed Autonomy Traffic Signalized Intersection

November 13, 2022

Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
Chanyoung Park, Soohyun Park, Gyu Seon Kim, Soyi Jung, Jae-Hyun Kim, Joongheon Kim
Multi Agent Deep Reinforcement Learning Transportation System Urban Air Mobility MADDPG Algorithm Future Transportation Vertical Takeoff

October 3, 2022

Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control
Chanyoung Park, Soohyun Park, Soyi Jung, Carlos Cordeiro, Joongheon Kim
Multi Agent Deep Reinforcement Learning Multiple Unmanned Aerial Vehicle Directional Communication

September 6, 2022

Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning
Min Hua, Zhi Li, Quan Zhou
Multi Agent Deep Reinforcement Learning Multi Agent Learning Energy Management Multi Agent Network Tractor Trailer Vehicle Future Transportation

August 30, 2022

A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space
Hongzhi Hua, Guixuan Wen, Kaigui Wu
Deep Reinforcement Learning Environment Exploration Action Space Multi Agent Deep Reinforcement Learning Multi Agent Particle Multi Agent Deep Deterministic Policy

August 7, 2022

Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning
Kai Liu, Tianxian Zhang, Lingjiang Kong
Multi Agent Multi Agent Deep Reinforcement Learning Value Decomposition Soft QMIX Maximum Correntropy Criterion

July 23, 2022

Halftoning with Multi-Agent Deep Reinforcement Learning
Haitian Jiang, Dongliang Xiong, Xiaowen Jiang, Aiguo Yin, Li Ding, Kai Huang
Convolutional Layer Multi Agent Deep Reinforcement Learning High Quality Halftone Inverse Halftoning

July 8, 2022

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Shunyu Liu, Jie Song, Yihe Zhou, Na Yu, Kaixuan Chen, Zunlei Feng, Mingli Song
Multi Agent Reinforcement Learning Multi Agent Deep Reinforcement Learning Multi Agent Learning interactiOn Pattern disAgreement Score

June 24, 2022

Multi-Agent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing
Shaoyang Wang, Chau Yuen, Wei Ni, Guan Yong Liang, Tiejun Lv
Reinforcement Learning Deep Reinforcement Learning Multi Agent Multi Agent Deep Reinforcement Learning Routing Problem Network Function

Multi Agent Deep Reinforcement Learning

Papers

Multi-vehicle Platoon Overtaking Using NoisyNet Multi-Agent Deep Q-Learning Network

Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement

Securing IoT Communication using Physical Sensor Data -- Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework

Coordination for Connected and Automated Vehicles at Non-signalized Intersections: A Value Decomposition-based Multiagent Deep Reinforcement Learning Approach

Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility

Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control

Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning

A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space

Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning

Halftoning with Multi-Agent Deep Reinforcement Learning

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

Multi-Agent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing