Nicolas Heess

发表

Learning Skill Embeddings for Transferable Robot Skills

Martin A. Riedmiller, Jost Tobias Springenberg, Ziyu Wang, 2017, NIPS 2017.

Regularized Hierarchical Policies for Compositional Transfer in Robotics pdf

Martin A. Riedmiller, Thomas Lampe, Abbas Abdolmaleki, 2019, ArXiv.

learn continuous control with deep bestärkendem

Yuval Tassa, David Silver, Nicolas Heess, 2016 .

Transferring Task Goals via Hierarchical Reinforcement Learning

Yee Whye Teh, Razvan Pascanu, Shaobo Hou, 2018 .

Value constrained model-free continuous control pdf

Raia Hadsell, Steven Bohez, Jonas Buchli, 2019, ArXiv.

Imagination-Augmented Agents for Deep Reinforcement Learning

Razvan Pascanu, Demis Hassabis, David Silver, 2017, NIPS.

Distributed Distributional Deterministic Policy Gradients pdf

Matthew W. Hoffman, David Budden, Nicolas Heess, 2018, ICLR.

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs pdf

Nicolas Le Roux, Nicolas Heess, John M. Winn, 2011, ICANN.

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

Geoffrey E. Hinton, Yuval Tassa, Koray Kavukcuoglu, 2016, NIPS.

Learning human behaviors from motion capture by adversarial imitation pdf

Yuval Tassa, Ziyu Wang, Nicolas Heess, 2017, ArXiv.

Hierarchical visuomotor control of humanoids pdf

Nicolas Heess, Dhruva Tirumala, Arun Ahuja, 2018, ICLR.

Learning and Transfer of Modulated Locomotor Controllers pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2016, ArXiv.

Composing Entropic Policies using Divergence Correction

Nicolas Heess, Jonathan J. Hunt, Timothy Lillicrap, 2018, ICML.

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures pdf

Pushmeet Kohli, Csaba Szepesvári, Krishnamurthy Dvijotham, 2018, ICLR.

Continuous control with deep reinforcement learning pdf

Yuval Tassa, David Silver, Daan Wierstra, 2015, ICLR.

Meta-learning of Sequential Strategies pdf

Yee Whye Teh, Razvan Pascanu, Nando de Freitas, 2019, ArXiv.

Memory-based control with recurrent neural networks pdf

David Silver, Nicolas Heess, Jonathan J. Hunt, 2015, ArXiv.

Learning to Pass Expectation Propagation Messages

Daniel Tarlow, Nicolas Heess, John M. Winn, 2013, NIPS.

Metacontrol for Adaptive Imagination-Based Optimization pdf

Razvan Pascanu, Jessica B. Hamrick, Oriol Vinyals, 2017, ICLR.

Robust Imitation of Diverse Behaviors

Nando de Freitas, Ziyu Wang, Nicolas Heess, 2017, NIPS.

Searching for objects driven by context

Yee Whye Teh, Nicolas Heess, Vittorio Ferrari, 2012, NIPS.

Reinforced Variational Inference

David Silver, Nicolas Heess, John Schulman, 2015, NIPS 2015.

Relational inductive biases, deep learning, and graph networks pdf

Razvan Pascanu, H. Francis Song, Jessica B. Hamrick, 2018, ArXiv.

Direction Opponency, Not Quadrature, Is Key to the 1/4 Cycle Preference for Apparent Motion in the Motion Energy Model

Nicolas Heess, Wyeth Bair, N. Heess, 2010, The Journal of Neuroscience.

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search pdf

Nicolas Heess, Lars Buesing, Arthur Guez, 2018, ICLR.

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation pdf

Yuval Tassa, Martin A. Riedmiller, Thomas Lampe, 2017, ArXiv.

Sim-to-Real Robot Learning from Pixels with Progressive Nets pdf

Razvan Pascanu, Raia Hadsell, Nicolas Heess, 2016, CoRL.

Stabilizing Transformers for Reinforcement Learning

Razvan Pascanu, H. Francis Song, Raia Hadsell, 2019, ICML.

Actor-Critic Reinforcement Learning with Energy-Based Policies

Yee Whye Teh, David Silver, Nicolas Heess, 2012, EWRL.

Maximum a Posteriori Policy Optimisation pdf

Yuval Tassa, Rémi Munos, Martin A. Riedmiller, 2018, ICLR.

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions pdf

Nicolas Heess, Lars Buesing, Theophane Weber, 2019, AISTATS.

Learning Robot Skill Embeddings

Karol Hausman, Martin A. Riedmiller, Jost Tobias Springenberg, 2017, NIPS 2017.

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL pdf

Yee Whye Teh, Razvan Pascanu, Hyeonwoo Noh, 2019, ArXiv.

Sample Efficient Actor-Critic with Experience Replay pdf

Nando de Freitas, Rémi Munos, Koray Kavukcuoglu, 2016, ICLR.

Passing Expectation Propagation Messages with Kernel Methods pdf

Arthur Gretton, Wittawat Jitkrittum, Nicolas Heess, 2015, ArXiv.

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems

Tom Eccles, Guy Lever, Joel Z. Leibo, 2019, Artificial Life Conference Proceedings.

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces pdf

Tamir Hazan, Daniel Tarlow, Nicolas Heess, 2019, NeurIPS.

Learning an Embedding Space for Transferable Robot Skills

Karol Hausman, Martin A. Riedmiller, Jost Tobias Springenberg, 2018, ICLR.

Emergent Coordination Through Competition pdf

Guy Lever, Siqi Liu, Thore Graepel, 2019, ICLR.

Learning Hierarchical Information Flow with Recurrent Neural Modules

James Davidson, Nicolas Heess, Danijar Hafner, 2017, NIPS.

Emergence of Locomotion Behaviours in Rich Environments pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2017, ArXiv.

Credit Assignment Techniques in Stochastic Computation Graphs

David Silver, Nicolas Heess, Lars Buesing, 2019, AISTATS.

Structure & Priors in Reinforcement Learning (SPiRL)

Thomas L. Griffiths, Junhyuk Oh, Abhishek Gupta, 2019, ICLR 2019.

Quinoa: a Q-function You Infer Normalized Over Actions pdf

Martin A. Riedmiller, Abbas Abdolmaleki, Jost Tobias Springenberg, 2019, ArXiv.

Reinforcement and Imitation Learning for Diverse Visuomotor Skills pdf

Nando de Freitas, Raia Hadsell, Yuke Zhu, 2018, Robotics: Science and Systems.

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards pdf

Martin A. Riedmiller, Thomas Lampe, Olivier Pietquin, 2017, ArXiv.

Self-supervised Learning of Image Embedding for Continuous Control pdf

Martin A. Riedmiller, Jost Tobias Springenberg, Nicolas Heess, 2019, ArXiv.

Learning by Playing - Solving Sparse Reward Tasks from Scratch

Martin A. Riedmiller, Thomas Lampe, Jost Tobias Springenberg, 2018, ICML.

Reusable neural skill embeddings for vision-guided whole body movement and object manipulation pdf

Yuval Tassa, Nicolas Heess, Tom Erez, 2019, ArXiv.

Deterministic Policy Gradient Algorithms

Guy Lever, Martin A. Riedmiller, David Silver, 2014, ICML.

Particle Value Functions pdf

Yee Whye Teh, Arnaud Doucet, George Tucker, 2017, ICLR.

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control pdf

H. Francis Song, Martin A. Riedmiller, Abbas Abdolmaleki, 2019, ICLR.

Relative Entropy Regularized Policy Iteration pdf

Yuval Tassa, Steven Bohez, Martin A. Riedmiller, 2018, ArXiv.

Learning Continuous Control Policies by Stochastic Value Gradients

Yuval Tassa, David Silver, Nicolas Heess, 2015, NIPS.

Graph networks as learnable physics engines for inference and control pdf

Raia Hadsell, Martin A. Riedmiller, Jost Tobias Springenberg, 2018, ICML.

Learning a Generative Model of Images by Factoring Appearance and Shape

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, 2011, Neural Computation.

Information asymmetry in KL-regularized RL pdf

Yee Whye Teh, Razvan Pascanu, Wojciech Czarnecki, 2019, ICLR.

The Shape Boltzmann Machine: A strong model of object shape

Nicolas Heess, John M. Winn, S. M. Ali Eslami, 2012, CVPR.

Success at any cost: value constrained model-free continuous control

Raia Hadsell, Steven Bohez, Jonas Buchli, 2018 .

Just-In-Time Kernel Regression for Expectation Propagation

Arthur Gretton, Wittawat Jitkrittum, Zoltán Szabó, 2015 .

Hindsight Credit Assignment

Doina Precup, Satinder Singh, Nicolas Heess, 2019, NeurIPS.

Learning Generative Texture Models with extended Fields-of-Experts

Geoffrey E. Hinton, Christopher K. I. Williams, Nicolas Heess, 2009, BMVC.

Filtering Variational Objectives

Yee Whye Teh, Arnaud Doucet, Mohammad Norouzi, 2017, NIPS.

The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution

Guy Lever, Pushmeet Kohli, Chrisantha Fernando, 2019, AAMAS.

The Shape Boltzmann Machine: A Strong Model of Object Shape

Christopher K. I. Williams, Nicolas Heess, John M. Winn, 2012, International Journal of Computer Vision.

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics

Christopher K. I. Williams, Nicolas Heess, Jyri Kivinen, 2014 .

Value-driven Hindsight Modelling pdf

Fabio Viola, Doina Precup, David Silver, 2020, NeurIPS.

Mix&Match - Agent Curricula for Reinforcement Learning pdf

Yee Whye Teh, Razvan Pascanu, Max Jaderberg, 2018, ICML.

FeUdal Networks for Hierarchical Reinforcement Learning

Tom Schaul, Max Jaderberg, Simon Osindero, 2017, ICML.

Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction pdf

Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, 2018, ArXiv.

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Martin A. Riedmiller, Thomas Lampe, Abbas Abdolmaleki, 2019, CoRL.

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection

Christopher K. I. Williams, Nicolas Heess, Jyri J. Kivinen, 2014, AISTATS.

Compositional Transfer in Hierarchical Reinforcement Learning

Thomas Lampe, Abbas Abdolmaleki, Jost Tobias Springenberg, 2019, RSS 2020.

Gradient Estimation Using Stochastic Computation Graphs

Pieter Abbeel, Nicolas Heess, John Schulman, 2015, NIPS.

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

Arthur Gretton, Wittawat Jitkrittum, Zoltán Szabó, 2015, UAI.

Bayes-Adaptive Simulation-based Search with Value Function Approximation

Peter Dayan, David Silver, Nicolas Heess, 2014, NIPS.

Meta reinforcement learning as task inference pdf

Yee Whye Teh, Nicolas Heess, Alexandre Galashov, 2019, ArXiv.

Learning model-based planning from scratch pdf

Razvan Pascanu, Yujia Li, Oriol Vinyals, 2017, ArXiv.

Thalamus Gated Recurrent Modules

James Davidson, Nicolas Heess, Danijar Hafner, 2017, NIPS 2017.

Recurrent Models of Visual Attention

Alex Graves, Koray Kavukcuoglu, Nicolas Heess, 2014, NIPS.

Neural probabilistic motor primitives for humanoid control pdf

Yee Whye Teh, Nicolas Heess, Alexandre Galashov, 2018, ICLR.

Unsupervised Learning of 3D Structure from Images

Max Jaderberg, Nicolas Heess, Shakir Mohamed, 2016, NIPS.

Distral: Robust multitask reinforcement learning

Yee Whye Teh, Razvan Pascanu, Raia Hadsell, 2017, NIPS.

A Generalized Training Approach for Multiagent Learning pdf

Guy Lever, Thore Graepel, Karl Tuyls, 2020, ICLR.

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Martin A. Riedmiller, Thomas Lampe, Jonas Buchli, 2020, CoRL.

The Termination Critic

Doina Precup, Rémi Munos, Nicolas Heess, 2019, AISTATS.

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner pdf

Steven Bohez, Philemon Brakel, Nicolas Heess, 2021, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning pdf

Sandy H. Huang, Raia Hadsell, Martin A. Riedmiller, 2021, ArXiv.

Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies pdf

Martina Zambelli, Raia Hadsell, Dushyant Rao, 2021, ArXiv.

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion

Martin A. Riedmiller, Viorica Patraucean, Nicolas Heess, 2021, CoRL.

From Motor Control to Team Play in Simulated Humanoid Football pdf

Yuval Tassa, Guy Lever, Thore Graepel, 2021, Sci. Robotics.

Data-efficient Hindsight Off-policy Option Learning pdf

Dushyant Rao, Abbas Abdolmaleki, Nicolas Heess, 2021, ICML.

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Marcus Hutter, Nicolas Heess, Fabio Viola, 2020, ICML.

Evaluating model-based planning and planner amortization for continuous control pdf

Yuval Tassa, Alessandro Davide Ialongo, Martin A. Riedmiller, 2021, ICLR.

Collect & Infer - a fresh look at data-efficient Reinforcement Learning

Martin A. Riedmiller, Jost Tobias Springenberg, Nicolas Heess, 2021, CoRL.

A Constrained Multi-Objective Reinforcement Learning Framework

Sandy H. Huang, Yuval Tassa, Raia Hadsell, 2021, CoRL.

Learning Dynamics Models for Model Predictive Agents pdf

Yuval Tassa, Gabriel Dulac-Arnold, Nicolas Heess, 2021, ArXiv.

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration pdf

Martin A. Riedmiller, Nicolas Heess, Martin Riedmiller, 2021, ArXiv.

Offline Meta-Reinforcement Learning for Industrial Insertion pdf

Oleg O. Sushkov, Sergey Levine, Stefan Schaal, 2021, ArXiv.

Neural Production Systems pdf

Charles Blundell, Nicolas Heess, Nan Rosemary Ke, 2021, ArXiv.

Value-driven Hindsight Modelling pdf

Doina Precup, David Silver, Nicolas Heess, 2020, NeurIPS.

dm_control: Software and Tasks for Continuous Control

Yuval Tassa, Steven Bohez, Nicolas Heess, 2020, Softw. Impacts.

Temporal Difference Uncertainties as a Signal for Exploration pdf

Razvan Pascanu, Nicolas Heess, Alexandre Galashov, 2020, ArXiv.