Active Reinforcement Learning – A Roadmap Towards Curious Classifier Systems for Self-Adaptation release_6dgept56bfg2bdiqvvlrlm6ha4

by Simon Reichhuber, Sven Tomforde

Released as a article .

2022  

Abstract

Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the learning problem and make isolated use of techniques from different field of machine learning such as reinforcement learning, active learning, anomaly detection or transfer learning, for instance. In this context, the fundamental reinforcement learning approaches come with several drawbacks that hinder their application to real-world systems: trial-and-error, purely reactive behaviour or isolated problem handling. The idea of this article is to present a concept for alleviating these drawbacks by setting up a research agenda towards what we call "active reinforcement learning" in intelligent systems.
In text/plain format

Archived Files and Locations

application/pdf  1.5 MB
file_4xn23shguvec7p7lq3q53ksx44
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-01-11
Version   v1
Language   en ?
arXiv  2201.03947v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 1d6b6261-dd96-4d41-a003-abbbed0c50c9
API URL: JSON