Machine Learning

, Volume 84, Issue 1, pp 7–49

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


DOI: 10.1007/s10994-011-5237-8

Cite this article as:
Kovacs, T. & Egginton, R. Mach Learn (2011) 84: 7. doi:10.1007/s10994-011-5237-8


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.


Reinforcement learningSoftware engineering
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© The Author(s) 2011

Authors and Affiliations

  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK