EVaDE inserts three Gaussian-dropout convolutional layers into SimPLe reward models, raising mean human-normalized Atari 100K score from 0.525 to 0.682 in the paper's runs.
Unifying Count-Based Explo- ration and Intrinsic Motivation
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EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
EVaDE inserts three Gaussian-dropout convolutional layers into SimPLe reward models, raising mean human-normalized Atari 100K score from 0.525 to 0.682 in the paper's runs.