no code implementations • 6 Jun 2023 • Marcin Andrychowicz, Lasse Espeholt, Di Li, Samier Merchant, Alexander Merose, Fred Zyda, Shreya Agrawal, Nal Kalchbrenner
The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages.
1 code implementation • 30 Nov 2021 • Piotr Januszewski, Mateusz Olko, Michał Królikowski, Jakub Świątkowski, Marcin Andrychowicz, Łukasz Kuciński, Piotr Miłoś
The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field.
1 code implementation • NeurIPS 2021 • Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
no code implementations • 25 May 2021 • Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
no code implementations • ICLR 2021 • Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
2 code implementations • 10 Jun 2020 • Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
2 code implementations • 16 Oct 2019 • OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, Lei Zhang
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.
no code implementations • 1 Aug 2018 • OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.
28 code implementations • 26 Feb 2018 • Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, Wojciech Zaremba
The purpose of this technical report is two-fold.
Ranked #1 on Reinforcement Learning (RL) on .
no code implementations • 18 Oct 2017 • Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel
While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator.
no code implementations • 18 Oct 2017 • Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel
By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.
Robotics Systems and Control
no code implementations • 17 Oct 2017 • Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.
3 code implementations • 28 Sep 2017 • Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL).
26 code implementations • NeurIPS 2017 • Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL).
10 code implementations • ICLR 2018 • Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
no code implementations • NeurIPS 2017 • Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.
8 code implementations • NeurIPS 2016 • Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
The move from hand-designed features to learned features in machine learning has been wildly successful.
no code implementations • 9 Feb 2016 • Marcin Andrychowicz, Karol Kurach
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM).
no code implementations • 19 Nov 2015 • Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine.
no code implementations • 8 May 2014 • Marcin Andrychowicz, Stefan Dziembowski, Daniel Malinowski, Łukasz Mazurek
We hope that our work can draw the attention of the researchers working on formal modeling to the problem of the Bitcoin contract verification, and spark off more research on this topic.
Cryptography and Security