Evolutionary reinforcement learning for vision-based general video game playing.

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dc.contributor.authorTupper, Adam
dc.date.accessioned2020-10-14T01:24:27Z
dc.date.available2020-10-14T01:24:27Z
dc.date.issued2020en
dc.description.abstractOver the past decade, video games have become increasingly utilised for research in artificial intelligence. Perhaps the most extensive use of video games has been as benchmark problems in the field of reinforcement learning. Part of the reason for this is because video games are designed to challenge humans, and as a result, developing methods capable of mastering them is considered a stepping stone to achieving human-level per- formance in real-world tasks. Of particular interest are vision-based general video game playing (GVGP) methods. These are methods that learn from pixel inputs and can be applied, without modification, across sets of games. One of the challenges in evolutionary computing is scaling up neuroevolution methods, which have proven effective at solving simpler reinforcement learning problems in the past, to tasks with high- dimensional input spaces, such as video games. This thesis proposes a novel method for vision-based GVGP that combines the representational learning power of deep neural networks and the policy learning benefits of neuroevolution. This is achieved by separating state representation and policy learning and applying neuroevolution only to the latter. The method, AutoEncoder-augmented NeuroEvolution of Augmented Topologies (AE-NEAT), uses a deep autoencoder to learn compact state representations that are used as input for policy networks evolved using NEAT. Experiments on a selection of Atari games showed that this approach can successfully evolve high-performing agents and scale neuroevolution methods that evolve both weights and topology to do- mains with high-dimensional inputs. Overall, the experiments and results demonstrate a proof-of-concept of this separated state representation and policy learning approach and show that hybrid deep learning and neuroevolution-based GVGP methods are a promising avenue for future research.en
dc.identifier.urihttps://hdl.handle.net/10092/101134
dc.identifier.urihttp://dx.doi.org/10.26021/10198
dc.languageEnglish
dc.language.isoen
dc.publisherUniversity of Canterburyen
dc.rightsAll Right Reserveden
dc.rights.urihttps://canterbury.libguides.com/rights/thesesen
dc.titleEvolutionary reinforcement learning for vision-based general video game playing.en
dc.typeTheses / Dissertationsen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorUniversity of Canterburyen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Scienceen
uc.bibnumber2957906en
uc.collegeFaculty of Engineeringen
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