35 citations · 103 across the 7 of their papers we have counts for
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cs.LG2019★ 3 cited
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Heecheol Kim, Masanori Yamada, Kosuke Miyoshi +1
One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions,…
stat.ML2019★ 1 cited
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
Masanori Yamada, Heecheol Kim, Kosuke Miyoshi +1
We propose the factorized action variational autoencoder (FAVAE), a state-of-the-art generative model for learning disentangled and interpretable representations from sequential da…