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Nicolas Heess — most-cited papers & profile · Reinforcement Learning
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·
overview
Nicolas Heess
87
papers ·
4958
citations ·
57
h-index
Google (United Kingdom)
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Most-cited papers
Emergence of Locomotion Behaviours in Rich Environments
2017 · 670 citations
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
2017 · 510 citations
Distributed Distributional Deterministic Policy Gradients
2018 · 286 citations
FeUdal Networks for Hierarchical Reinforcement Learning
2017 · 252 citations
Sample Efficient Actor-Critic with Experience Replay
2016 · 222 citations
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
2018 · 215 citations
Imagination-Augmented Agents for Deep Reinforcement Learning
2017 · 211 citations
Distral: Robust Multitask Reinforcement Learning
2017 · 183 citations
Maximum a Posteriori Policy Optimisation
2018 · 167 citations
Learning human behaviors from motion capture by adversarial imitation
2017 · 155 citations
Learning by Playing - Solving Sparse Reward Tasks from Scratch
2018 · 155 citations
Stabilizing Transformers for Reinforcement Learning
2019 · 132 citations
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
2017 · 118 citations
Sim-to-Real Robot Learning from Pixels with Progressive Nets
2016 · 109 citations
Critic Regularized Regression
2020 · 90 citations
Top co-authors
Martin Riedmiller
· 28
Jost Tobias Springenberg
· 18
Abbas Abdolmaleki
· 16
Leonard Hasenclever
· 13
Roland Hafner
· 13
Josh Merel
· 10
Arunkumar Byravan
· 9
Lars Buesing
· 9
Markus Wulfmeier
· 9
Razvan Pascanu
· 9
Thomas Lampe
· 9
Arthur Guez
· 8
Topics
Model-Based RL
Policy Gradient
Value-Based
Meta-RL
Offline RL
Exploration
Game AI
Multi-Agent
Safe RL