28 citations · 35 across the 7 of their papers we have counts for
5 papers · 1 filter
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models
Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski
How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating…
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…
Expert-augmented actor-critic for ViZDoom and Montezumas Revenge
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. In…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…
Learning from the memory of Atari 2600
Jakub Sygnowski, Henryk Michalewski
We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600. We consider four models…