Search Results for author: Piotr Miłoś

Found 22 papers, 15 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

Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

no code implementations22 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.

Automated Theorem Proving

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

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.

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

Expert-augmented actor-critic for ViZDoom and Montezumas Revenge

2 code implementations10 Sep 2018 Michał Garmulewicz, Henryk Michalewski, Piotr Miłoś

We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite.

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