A New Approach for Multi-agent Reinforcement Learning

2020 ◽  
pp. 263-275
Author(s):  
Elmehdi Amhraoui ◽  
Tawfik Masrour
Keyword(s):  
Reinforcement Learning ◽  
New Approach ◽  
Multi Agent

Research on Path-Planning of Manipulator Based on Multi-Agent Reinforcement Learning

2010 ◽  
Vol 44-47 ◽  
pp. 2116-2120
Author(s):  
Liang Tong
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Dynamic Characteristic ◽  
Simulation Experiment ◽  
Robot Manipulator ◽  
Variable Structure ◽  
Learning Method ◽  
New Approach ◽  
Multi Agent ◽  
Kohonen Net

Because of the dynamic characteristic of high nonlinear,strong coupling and variable structure,it is difficult to perform effective controlling on the robot manipulator by conventional controlling theory.In this paper,a new approach of multi-agent reinforcement learning method based on Kohonen net is proposed which is used in the multi-agent environment of robot manipulator path-planning and the simulation experiment shows the validity of this method.

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group

Multi-Agent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xiaoyu Zhu ◽  
Yueyi Luo ◽  
Anfeng Liu ◽  
Md Zakirul Alam Bhuiyan ◽  
Shaobo Zhang
Keyword(s):  
Reinforcement Learning ◽  
Computation Offloading ◽  
Multi Agent

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

Sign in / Sign up

Export Citation Format

Share Document