Search Results for author: Piotr Kozakowski

Found 10 papers, 6 papers with code

tsGT: Stochastic Time Series Modeling With Transformer

no code implementations8 Mar 2024 Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski, Łukasz Maziarka, Marta Emilia Nowakowska, Łukasz Kaiser, Piotr Miłoś

Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data.

Time Series

Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction

no code implementations11 Oct 2023 Mikołaj Sacha, Michał Sadowski, Piotr Kozakowski, Ruard van Workum, Stanisław Jastrzębski

Retrosynthesis involves determining a sequence of reactions to synthesize complex molecules from simpler precursors.

Retrosynthesis

Q-Value Weighted Regression: Reinforcement Learning with Limited Data

1 code implementation12 Feb 2021 Piotr Kozakowski, Łukasz Kaiser, Henryk Michalewski, Afroz Mohiuddin, Katarzyna Kańska

QWR is an extension of Advantage Weighted Regression (AWR), an off-policy actor-critic algorithm that performs very well on continuous control tasks, also in the offline setting, but has low sample efficiency and struggles with high-dimensional observation spaces.

Atari Games Continuous Control +4

Planning and Learning Using Adaptive Entropy Tree Search

1 code implementation12 Feb 2021 Piotr Kozakowski, Mikołaj Pacek, Piotr Miłoś

We present Adaptive Entropy Tree Search (ANTS) - a novel algorithm combining planning and learning in the maximum entropy paradigm.

Model Based Reinforcement Learning for Atari

no code implementations ICLR 2020 Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Model-based Reinforcement Learning +3

Uncertainty-sensitive Learning and Planning with Ensembles

1 code implementation19 Dec 2019 Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski, Piotr Kozakowski, Maciek Klimek

The former manifests itself through the use of value function, while the latter is powered by a tree search planner.

Montezuma's Revenge

Forecasting Deep Learning Dynamics with Applications to Hyperparameter Tuning

no code implementations25 Sep 2019 Piotr Kozakowski, Łukasz Kaiser, Afroz Mohiuddin

Concretely, we introduce a forecasting model that, given a hyperparameter schedule (e. g., learning rate, weight decay) and a history of training observations (such as loss and accuracy), predicts how the training will continue.

Language Modelling

Uncertainty - sensitive learning and planning with ensembles

1 code implementation25 Sep 2019 Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski, Piotr Kozakowski, Maciej Klimek

Notably, our method performs well in environments with sparse rewards where standard $TD(1)$ backups fail.

Montezuma's Revenge

Model-Based Reinforcement Learning for Atari

2 code implementations1 Mar 2019 Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Atari Games 100k +4

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