Feature Reinforcement Learning: State of the Art
Mayank Daswani, Peter Sunehag, Marcus Hütter · ANU Open Research (Australian National University) · 2014
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.