Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
A survey of inverse reinforcement learning
Adams S., Cody T., Beling P. Artificial Intelligence Review55 (6):4307-4346,2022.Type:Article
Date Reviewed: May 1 2023

The process of discovering and incorporating knowledge from educators into machine learning (ML) poses major challenges. Beyond coding the known or derived characteristics of systems into ML algorithms, how should innovative researchers discover, design, and implement algorithms for diverse, complex artificial intelligence (AI) applications? Adams et al. present a pertinent, comprehensive literature review for understanding inverse reinforcement learning (IRL) in complicated AI applications.

The authors introduce formal Markov decision processes (MDPs) for understanding how sequential decisions work in optimum policy learning algorithms such as inverse optimal control (IOC) and apprenticeship learning algorithms. It is important that readers understand the roles of states, actions, transformations, and rewards in alternative models, as well as the use of MDPs in a variety of IRL applications. The authors’ great efforts help to make the elements of MDPs in complex AI application environments accessible to non-mathematically inclined audiences.

Undeniably, reinforcement learning can be used to ascertain the best possible control policies for AI applications, though at the expense of enormous discovery interactions in intricate environments. However, IRL might be used to learn the constraints and constructs of concise depictions of tasks from expert knowledge, and then applying it to optimal control policies in complex AI applications.

The decision on when to use reinforcement learning or IRL is not straightforward. It is important to understand the variations, similarities, and effectiveness of the current IOC and IRL algorithms. This engaging literature review is recommended for AI practitioners interested in IRL and creating more revolutionary global AI applications.

Reviewer:  Amos Olagunju Review #: CR147584 (2307-0093)
Bookmark and Share
  Featured Reviewer  
 
Learning (I.2.6 )
 
 
Applications And Expert Systems (I.2.1 )
 
 
General (I.2.0 )
 
Would you recommend this review?
yes
no
Other reviews under "Learning": Date
Learning in parallel networks: simulating learning in a probabilistic system
Hinton G. (ed) BYTE 10(4): 265-273, 1985. Type: Article
Nov 1 1985
Macro-operators: a weak method for learning
Korf R. Artificial Intelligence 26(1): 35-77, 1985. Type: Article
Feb 1 1986
Inferring (mal) rules from pupils’ protocols
Sleeman D.  Progress in artificial intelligence (, Orsay, France,391985. Type: Proceedings
Dec 1 1985
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy