Adaptive Gait Acquisition Using Multi-Agent Learning for Wall Climbing Robots

Proceedings of the 12th International Symposium on Automation and Robotics in Construction (ISARC) ◽

10.22260/isarc1995/0040 ◽

1995 ◽

Cited By ~ 3

Author(s):

Lawrence Bull ◽

Terence C Fogarty ◽

Sadayoshi Mikami ◽

James G Thomas

Keyword(s):

Climbing Robots ◽

Agent Learning ◽

Multi Agent ◽

Adaptive Gait

Download Full-text

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

IEEE Access ◽

10.1109/access.2021.3087410 ◽

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

Download Full-text

Hierarchical Reinforcement Learning

ACM Computing Surveys ◽

10.1145/3453160 ◽

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.

Download Full-text