
Dataefficient Hindsight Offpolicy Option Learning
Solutions to most complex tasks can be decomposed into simpler, intermed...
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Simple Sensor Intentions for Exploration
Modern reinforcement learning algorithms can learn solutions to increasi...
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A Distributional View on MultiObjective Policy Optimization
Many realworld problems require trading off multiple competing objectiv...
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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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ContinuousDiscrete Reinforcement Learning for Hybrid Control in Robotics
Many realworld control problems involve both discrete decision variable...
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Quinoa: a Qfunction You Infer Normalized Over Actions
We present an algorithm for learning an approximate actionvalue soft Q...
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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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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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Regularized Hierarchical Policies for Compositional Transfer in Robotics
The successful application of flexible, general learning algorithms  s...
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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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Simultaneously Learning Vision and Featurebased Control Policies for Realworld BallinaCup
We present a method for fast training of vision based control policies o...
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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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Relative Entropy Regularized Policy Iteration
We present an offpolicy actorcritic algorithm for Reinforcement Learni...
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Maximum a Posteriori Policy Optimisation
We introduce a new algorithm for reinforcement learning called Maximum a...
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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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Learning by Playing  Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SACX), a new learning paradigm ...
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DeepMind Control Suite
The DeepMind Control Suite is a set of continuous control tasks with a s...
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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
We propose a general and modelfree approach for Reinforcement Learning ...
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Emergence of Locomotion Behaviours in Rich Environments
The reinforcement learning paradigm allows, in principle, for complex be...
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PVEs: PositionVelocity Encoders for Unsupervised Learning of Structured State Representations
We propose positionvelocity encoders (PVEs) which learnwithout super...
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Learning and Transfer of Modulated Locomotor Controllers
We study a novel architecture and training procedure for locomotion task...
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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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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Deep convolutional networks have proven to be very successful in learnin...
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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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Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
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Martin Riedmiller
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Research Scientist at Google DeepMind