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Feature reinforcement learning: state of the art

Daswani, Mayank; Sunehag, Peter; Hutter, Marcus


Feature reinforcement learning was introduced five years ago as a principled and practical approach to history-based learning. This paper examines the progress since its inception. We now have both model-based and model-free cost functions, most recently extended to the function approximation setting. Our current work is geared towards playing ATARI games using imitation learning, where we use Feature RL as a feature selection method for high-dimensional domains.

CollectionsANU Research Publications
Date published: 2014-07-28
Type: Conference paper


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