Search Results for author: Łukasz Kuciński

Found 13 papers, 9 papers with code

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations24 Nov 2022 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.

Causal Discovery Experimental Design

Disentangling Transfer in Continual Reinforcement Learning

no code implementations28 Sep 2022 Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, limiting the applicability of continual learning solutions to realistic scenarios.

Continual Learning Continuous Control +2

Subgoal Search For Complex Reasoning Tasks

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.

Rubik's Cube

Continual World: A Robotic Benchmark For Continual Reinforcement Learning

1 code implementation NeurIPS 2021 Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents.

Continual Learning reinforcement-learning +1

Emergence of compositional language in communication through noisy channel

no code implementations ICML Workshop LaReL 2020 Łukasz Kuciński, Paweł Kołodziej, Piotr Miłoś

In this paper, we investigate how communication through a noisy channel can lead to the emergence of compositional language.

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

Developmentally motivated emergence of compositional communication via template transfer

1 code implementation4 Oct 2019 Tomasz Korbak, Julian Zubek, Łukasz Kuciński, Piotr Miłoś, Joanna Rączaszek-Leonardi

This paper explores a novel approach to achieving emergent compositional communication in multi-agent systems.

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

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