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no code implementations • 8 Feb 2024 • Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin Riedmiller

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.

no code implementations • 16 Jan 2024 • Konrad Zolna, Serkan Cabi, Yutian Chen, Eric Lau, Claudio Fantacci, Jurgis Pasukonis, Jost Tobias Springenberg, Sergio Gomez Colmenarejo

As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them.

no code implementations • 20 Jun 2023 • Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Żołna, Scott Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Tom Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess

With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.

no code implementations • 18 May 2023 • Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess

We investigate the use of transformer sequence models as dynamics models (TDMs) for control.

no code implementations • 24 Feb 2023 • Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin Riedmiller

We conduct a set of experiments in the RGB-stacking environment, showing that planning with the learned skills and the associated model can enable zero-shot generalization to new tasks, and can further speed up training of policies via reinforcement learning.

3 code implementations • DeepMind 2022 • Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.

Ranked #1 on Skill Generalization on RGB-Stacking

no code implementations • 6 May 2022 • Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki, Konstantinos Bousmalis

Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.

1 code implementation • 21 Apr 2022 • Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, SiQi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin Riedmiller

Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.

1 code implementation • 12 Oct 2021 • Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori

We study the problem of robotic stacking with objects of complex geometry.

Ranked #2 on Skill Generalization on RGB-Stacking

no code implementations • ICLR 2022 • Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches.

no code implementations • 23 Aug 2021 • Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess

This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency.

no code implementations • 15 Jun 2021 • Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller

Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.

no code implementations • 23 Jan 2021 • William F. Whitney, Michael Bloesch, Jost Tobias Springenberg, Abbas Abdolmaleki, Kyunghyun Cho, Martin Riedmiller

This causes BBE to be actively detrimental to policy learning in many control tasks.

1 code implementation • NeurIPS 2020 • Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

From this perspective, we hypothesise that instabilities in training GANs arise from the integration error in discretising the continuous dynamics.

no code implementations • 16 Oct 2020 • Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.

no code implementations • 12 Oct 2020 • Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

5 code implementations • NeurIPS 2020 • Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.

no code implementations • 15 May 2020 • Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess

In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

no code implementations • ICLR 2020 • Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller

In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.

no code implementations • 19 Feb 2020 • Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin Riedmiller

In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.

no code implementations • 2 Jan 2020 • Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.

no code implementations • 5 Nov 2019 • Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin Riedmiller

We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form.

no code implementations • 9 Oct 2019 • Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.

Model-based Reinforcement Learning
Reinforcement Learning (RL)
**+2**

1 code implementation • ICLR 2020 • H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Si-Qi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M. Botvinick

Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.

no code implementations • 26 Jun 2019 • Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Tim Hertweck, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.

General Reinforcement Learning
Hierarchical Reinforcement Learning
**+4**

no code implementations • ICLR 2020 • Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.

2 code implementations • 18 May 2019 • Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Maximilian Dippel, Marius Lindauer, Frank Hutter

Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks.

1 code implementation • 3 Jan 2019 • Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, Martin Riedmiller

Operating directly from raw high dimensional sensory inputs like images is still a challenge for robotic control.

1 code implementation • 5 Dec 2018 • Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller

Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme.

3 code implementations • ICLR 2018 • Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective.

1 code implementation • ICML 2018 • Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model.

1 code implementation • ICML 2018 • Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL).

no code implementations • ICLR 2018 • Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space.

5 code implementations • 15 Mar 2017 • Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode

no code implementations • 16 Dec 2016 • Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard

We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances.

no code implementations • 2 Dec 2016 • Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution.

1 code implementation • NeurIPS 2016 • Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter

Bayesian optimization is a prominent method for optimizing expensive to evaluate black-box functions that is prominently applied to tuning the hyperparameters of machine learning algorithms.

5 code implementations • 19 Nov 2015 • Jost Tobias Springenberg

Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model.

Ranked #7 on Unsupervised Image Classification on MNIST

2 code implementations • 24 Jul 2015 • Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin Riedmiller, Wolfram Burgard

Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications.

1 code implementation • NeurIPS 2015 • Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images.

1 code implementation • CVPR 2015 • Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color.

1 code implementation • NIPS 2015 2015 • Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

Supplementary Material for Efficient and Robust Automated Machine Learning

37 code implementations • 21 Dec 2014 • Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller

Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers.

Ranked #117 on Image Classification on CIFAR-10

no code implementations • NeurIPS 2014 • Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox

Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training.

Ranked #84 on Image Classification on STL-10

2 code implementations • 21 Nov 2014 • Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko, Thomas Brox

We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color.

1 code implementation • 26 Jun 2014 • Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox

While such generic features cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

no code implementations • 20 Dec 2013 • Jost Tobias Springenberg, Martin Riedmiller

We present a probabilistic variant of the recently introduced maxout unit.

Ranked #184 on Image Classification on CIFAR-10 (using extra training data)

no code implementations • 18 Dec 2013 • Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches.

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