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VIME: Variational Information Maximizing Exploration
[article]
2017
arXiv
pre-print
Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration
arXiv:1605.09674v4
fatcat:lrwm2ssr7nb3dhrektnzymohuu