401 citations · 407 across the 5 of their papers we have counts for
5 papers
tsGT: Stochastic Time Series Modeling With Transformer
Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko +5
Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic tr…
Q-Value Weighted Regression: Reinforcement Learning with Limited Data
Piotr Kozakowski, Łukasz Kaiser, Henryk Michalewski +2
Sample efficiency and performance in the offline setting have emerged as significant challenges of deep reinforcement learning. We introduce Q-Value Weighted Regression (QWR), a si…
Planning and Learning Using Adaptive Entropy Tree Search
Piotr Kozakowski, Mikołaj Pacek, Piotr Miłoś
Recent breakthroughs in Artificial Intelligence have shown that the combination of tree-based planning with deep learning can lead to superior performance. We present Adaptive Entr…
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…
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…