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

Tim Kovacs, Robert Egginton, On the analysis and design of software for reinforcement learning, with a survey of existing systems. Machine Learning, 84(1--2), pp. 7–49. February 2011. No electronic version available. External information

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.

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