Adversarial Approach to Prediction in Atari Games

Marian Andrecki; Nicholas Kenelm Taylor

Adversarial Approach to Prediction in Atari Games

Marian Andrecki, Nicholas Kenelm Taylor

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Abstract

Recent advances in neural networks have resulted in reliable predictions of observations in Atari games. The quality of predictions can be improved by addition of a generative adversarial approach to the training. This research investigates this development and the benefits of unsupervised predictive learning for reinforcement learning agents.

Original language	English
Number of pages	2
Publication status	Published - 7 Jun 2017
Event	2017 EPSRC CDT Student Conference – Oxford, Bristol and Edinburgh - Oxford, United Kingdom Duration: 7 Jun 2017 → 7 Jun 2017 http://aims.robots.ox.ac.uk/epsrc-cdt-student-conference-oxford-bristol-and-edinburgh/

Conference

Conference	2017 EPSRC CDT Student Conference – Oxford, Bristol and Edinburgh
Country/Territory	United Kingdom
City	Oxford
Period	7/06/17 → 7/06/17
Internet address	http://aims.robots.ox.ac.uk/epsrc-cdt-student-conference-oxford-bristol-and-edinburgh/

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title = "Adversarial Approach to Prediction in Atari Games",

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M3 - Paper

T2 - 2017 EPSRC CDT Student Conference – Oxford, Bristol and Edinburgh

Y2 - 7 June 2017 through 7 June 2017

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Adversarial Approach to Prediction in Atari Games

Abstract

Conference

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