Feature reinforcement learning: state of the art
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.
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|Daswani et al Feature Reinforcement Learning 2014.pdf||183.49 kB||Adobe PDF|
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