On the analysis and design of software for reinforcement learning, with a survey of existing systems

TMD Kovacs, RM Egginton

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)

Abstract

Reinforcement Learning (RL) is a very complex domain and software for RL is correspondingly complex. We analyse the scope, requirements, and potential for RL software, discuss relevant design issues, survey existing software, and make recommendations for designers. We argue that broad and flexible libraries of reusable software components are valuable from a scientific, as well as practical, perspective, as they allow precise control over experimental conditions, encourage comparison of alternative methods, and allow a fuller exploration of the RL domain.
Translated title of the contributionOn the analysis and design of software for reinforcement learning, with a survey of existing systems
Original languageEnglish
Article number-
Pages (from-to)7 - 49
Number of pages42
JournalMachine Learning
Volume1-2
DOIs
Publication statusPublished - Feb 2011

Bibliographical note

Publisher: Springer
Other identifier: 2001291

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