Cooperative Multi-agent Learning in a Large Dynamic Environment

2015 ◽  
pp. 155-166 ◽  
Author(s):  
Wiem Zemzem ◽  
Moncef Tagina
Keyword(s):  
Dynamic Environment ◽  
Agent Learning ◽  
Multi Agent

scholarly journals User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Giuseppe Caso ◽  
Ozgu Alay ◽  
Guido Carlo Ferrante ◽  
Luca De Nardis ◽  
Maria-Gabriella Di Benedetto ◽  
...  
Keyword(s):  
Game Theory ◽  
Learning Algorithms ◽  
Technology Selection ◽  
Access Technology ◽  
Radio Access Technology ◽  
Radio Access ◽  
Agent Learning ◽  
Multi Agent ◽  
User Centric ◽  
Radio Access Technology Selection

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

scholarly journals The possible and the impossible in multi-agent learning

2007 ◽  
Vol 171 (7) ◽  
pp. 429-433 ◽  
Author(s):  
H. Peyton Young
Keyword(s):  
Agent Learning ◽  
Multi Agent

AGENT-BASED SYSTEM ARCHITECTURE FOR DYNAMIC AND OPEN ENVIRONMENTS

2003 ◽  
Vol 02 (01) ◽  
pp. 105-133 ◽  
Author(s):  
ELHADI SHAKSHUKI ◽  
HAMADA GHENNIWA ◽  
MAHAMED KAMEL
Keyword(s):  
System Architecture ◽  
Rapid Growth ◽  
Dynamic Environment ◽  
Information Gathering ◽  
Information Resources ◽  
Architecture Design ◽  
Agent Based ◽  
Interface Agents ◽  
Multi Agent ◽  
Computing Environments

The rapid growth of the network-centered (Internet and Intranet) computing environments requires new architectures for information gathering systems. Typically, in these environments, the information resources are dynamic, heterogeneous and distributed. In addition, these computing environments are open, where information resources may be connected or disconnected at any time. This paper presents an architecture for a multi-agent information gathering system. The architecture includes three types of agents: interface, broker and resource agents. The interface agents interact with the users to fulfill their interests and preferences. The resource agents access and capture the content of the information resources. The broker agents facilitate cooperation among the information and the resource agents to achieve their desired goals. This paper provides the agents' architecture, design and implementations that enable them to cooperate, coordinate and communicate with each other to gather information in an open and dynamic environment.

The Role Of Cooperation in Multi-Agent Learning

CKBS ’90 ◽  
1991 ◽  
pp. 164-180 ◽  
Author(s):  
Sati S. Sian
Keyword(s):  
Agent Learning ◽  
Multi Agent

scholarly journals Deter and protect: crime modeling with multi-agent learning

2017 ◽  
Vol 4 (3) ◽  
pp. 155-169 ◽  
Author(s):  
Trevor R. Caskey ◽  
James S. Wasek ◽  
Anna Y. Franz
Keyword(s):  
Agent Learning ◽  
Multi Agent ◽  
Crime Modeling
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