401 citations · 427 across the 5 of their papers we have counts for
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cs.LG2019★ 6 cited
Uncertainty-sensitive Learning and Planning with Ensembles
Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski +2
We propose a reinforcement learning framework for discrete environments in which an agent makes both strategic and tactical decisions. The former manifests itself through the use o…
cs.LG2019★ 401 cited
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos +11
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically require…