Randomized Prior Functions For Deep Reinforcement Learning. Train q θ k = f θ k + p. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the.
[DL輪読会]Randomized Prior Functions for Deep Reinforcement Learning from www.slideshare.net
Train q θ k = f θ k + p. To demonstrate this effect, we perform a very simple experiment: Recently, the paper “randomized prior functions for deep reinforcement learning”, presented at neurips 2018, proposes a simple yet effective model for capturing uncertainty, by.
There Is A Growing Literature On Uncertainty Estimation For Deep Learning From Fixed Datasets, But Many Of The.
Dealing with uncertainty is essential for efficient reinforcement learning. To demonstrate this effect, we perform a very simple experiment: Train q θ k = f θ k + p.
Randomized Prior Functions For Deep Reinforcement Learning Ian Osband Deepmind [email protected] John Aslanides Deepmind [email protected] Albin Cassirer.
Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the. Recently, the paper “randomized prior functions for deep reinforcement learning”, presented at neurips 2018, proposes a simple yet effective model for capturing uncertainty, by.
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