1 code implementation • 1 Jun 2022 • Michał Zawalski, Michał Tyrolski, Konrad Czechowski, Tomasz Odrzygóźdź, Damian Stachura, Piotr Piękos, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan.
no code implementations • 22 May 2022 • Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik
Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8. 2\%$ of problems neither language models nor automated theorem provers are able to solve on their own.
Ranked #2 on Automated Theorem Proving on miniF2F-test
1 code implementation • NeurIPS 2021 • Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński, Michał Zawalski, Krzysztof Olejnik, Yuhuai Wu, Łukasz Kuciński, Piotr Miłoś
In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework.
1 code implementation • NeurIPS Workshop LMCA 2020 • Piotr Kozakowski, Piotr Januszewski, Konrad Czechowski, Łukasz Kuciński, Piotr Miłoś
Planning in large state spaces inevitably needs to balance depth and breadth of the search.
1 code implementation • NeurIPS Workshop LMCA 2020 • Konrad Czechowski, Tomasz Odrzygóźdź, Michał Izworski, Marek Zbysiński, Łukasz Kuciński, Piotr Miłoś
We propose $\textit{trust-but-verify}$ (TBV) mechanism, a new method which uses model uncertainty estimates to guide exploration.
Model-based Reinforcement Learning reinforcement-learning +1
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.
1 code implementation • 19 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.
1 code implementation • 25 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.
2 code implementations • 1 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.
Ranked #12 on Atari Games 100k on Atari 100k