
Quinoa: a Qfunction You Infer Normalized Over Actions
We present an algorithm for learning an approximate actionvalue soft Q...
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ContinuousDiscrete Reinforcement Learning for Hybrid Control in Robotics
Many realworld control problems involve both discrete decision variable...
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Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
Offpolicy reinforcement learning algorithms promise to be applicable in...
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Imagined Value Gradients: ModelBased Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an...
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Graph networks as learnable physics engines for inference and control
Understanding and interacting with everyday physical scenes requires ric...
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Relative Entropy Regularized Policy Iteration
We present an offpolicy actorcritic algorithm for Reinforcement Learni...
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Regularized Hierarchical Policies for Compositional Transfer in Robotics
The successful application of flexible, general learning algorithms  s...
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Deep learning with convolutional neural networks for EEG decoding and visualization
A revised version of this article is now available at Human Brain Mappin...
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Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
In this paper we consider the problem of robot navigation in simple maze...
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Asynchronous Stochastic Gradient MCMC with Elastic Coupling
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...
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Unsupervised and Semisupervised Learning with Categorical Generative Adversarial Networks
In this paper we present a method for learning a discriminative classifi...
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Improving Deep Neural Networks with Probabilistic Maxout Units
We present a probabilistic variant of the recently introduced maxout uni...
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Multimodal Deep Learning for Robust RGBD Object Recognition
Robust object recognition is a crucial ingredient of many, if not all, r...
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Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
We introduce Embed to Control (E2C), a method for model learning and con...
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Striving for Simplicity: The All Convolutional Net
Most modern convolutional neural networks (CNNs) used for object recogni...
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Learning to Generate Chairs, Tables and Cars with Convolutional Networks
We train generative 'upconvolutional' neural networks which are able to...
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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Deep convolutional networks have proven to be very successful in learnin...
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Unsupervised feature learning by augmenting single images
When deep learning is applied to visual object recognition, data augment...
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Learning by Playing  Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SACX), a new learning paradigm ...
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Maximum a Posteriori Policy Optimisation
We introduce a new algorithm for reinforcement learning called Maximum a...
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Selfsupervised Learning of Image Embedding for Continuous Control
Operating directly from raw high dimensional sensory inputs like images ...
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Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness  to perturbations ...
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VMPO: OnPolicy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Some of the most successful applications of deep reinforcement learning ...
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Jost Tobias Springenberg
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Staff Research Scientist at AlbertLudwigsUniversity Freiburg im Breisgau