AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Feature Reinforcement Learning: State of the Art
Mayank Daswani, Peter Sunehag, Marcus Hutter

Last modified: 2014-06-18

Abstract


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.

Keywords


feature selection; general reinforcement learning; imitation learning; reinforcement learning;

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