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Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
[article]
2016
arXiv
pre-print
High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for learning on Q-function residuals. And second, we propose an exploration strategy inspired by the principles of state abstraction and information acquisition under uncertainty. We
arXiv:1603.04119v1
fatcat:dn2jtdr4pncadc6sddkkcrytcm