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Leonard Hasenclever
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2020 – today
- 2024
- [j6]Tuomas Haarnoja
, Ben Moran
Learning agile soccer skills for a bipedal robot with deep reinforcement learning. Sci. Robotics 9(89) (2024) - [c14]Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier:
Replay across Experiments: A Natural Extension of Off-Policy RL. ICLR 2024 - [i28]Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Montserrat Gonzalez Arenas, Maria Attarian, Maria Bauzá, Matthew Bennice, Alex Bewley, Adil Dostmohamed, Chuyuan Kelly Fu, Nimrod Gileadi, Marissa Giustina, Keerthana Gopalakrishnan, Leonard Hasenclever, Jan Humplik, Jasmine Hsu, Nikhil J. Joshi, Ben Jyenis, J. Chase Kew, Sean Kirmani, Tsang-Wei Edward Lee, Kuang-Huei Lee, Assaf Hurwitz Michaely, Joss Moore, Ken Oslund, Dushyant Rao, Allen Z. Ren, Baruch Tabanpour, Quan Vuong, Ayzaan Wahid, Ted Xiao, Ying Xu, Vincent Zhuang, Peng Xu, Erik Frey, Ken Caluwaerts, Tingnan Zhang, Brian Ichter, Jonathan Tompson, Leila Takayama, Vincent Vanhoucke, Izhak Shafran, Maja J. Mataric, Dorsa Sadigh, Nicolas Heess, Kanishka Rao, Nik Stewart, Jie Tan, Carolina Parada:
Learning to Learn Faster from Human Feedback with Language Model Predictive Control. CoRR abs/2402.11450 (2024) - [i27]Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy H. Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin A. Riedmiller, Nicolas Heess:
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. CoRR abs/2405.02425 (2024) - [i26]Norman Di Palo, Leonard Hasenclever, Jan Humplik, Arunkumar Byravan:
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning. CoRR abs/2407.20798 (2024) - 2023
- [c13]Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia:
Language to Rewards for Robotic Skill Synthesis. CoRL 2023: 374-404 - [c12]Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess:
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields. ICRA 2023: 9362-9369 - [i25]Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin A. Riedmiller:
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains. CoRR abs/2302.12617 (2023) - [i24]Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess:
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. CoRR abs/2304.13653 (2023) - [i23]Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess:
A Generalist Dynamics Model for Control. CoRR abs/2305.10912 (2023) - [i22]Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia:
Language to Rewards for Robotic Skill Synthesis. CoRR abs/2306.08647 (2023) - [i21]Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin A. Riedmiller:
Towards A Unified Agent with Foundation Models. CoRR abs/2307.09668 (2023) - [i20]Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier:
Replay across Experiments: A Natural Extension of Off-Policy RL. CoRR abs/2311.15951 (2023) - 2022
- [j5]Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess:
Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. 23: 221:1-221:68 (2022) - [j4]Siqi Liu
From motor control to team play in simulated humanoid football. Sci. Robotics 7(69) (2022) - [c11]Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller:
Evaluating Model-Based Planning and Planner Amortization for Continuous Control. ICLR 2022 - [c10]Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell:
Learning transferable motor skills with hierarchical latent mixture policies. ICLR 2022 - [c9]Philémon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis:
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. IROS 2022: 10335-10342 - [d1]Siqi Liu
Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". Zenodo, 2022 - [i19]Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel, Fereshteh Sadeghi, Leonard Hasenclever, Yuval Tassa, Emilio Parisotto, Jan Humplik, Tuomas Haarnoja, Roland Hafner, Markus Wulfmeier, Michael Neunert, Ben Moran, Noah Y. Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel, Raia Hadsell, Nicolas Heess:
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors. CoRR abs/2203.17138 (2022) - [i18]Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess:
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields. CoRR abs/2210.04932 (2022) - 2021
- [i17]Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess:
From Motor Control to Team Play in Simulated Humanoid Football. CoRR abs/2105.12196 (2021) - [i16]Michael Lutter, Leonard Hasenclever, Arunkumar Byravan, Gabriel Dulac-Arnold, Piotr Trochim, Nicolas Heess, Josh Merel, Yuval Tassa:
Learning Dynamics Models for Model Predictive Agents. CoRR abs/2109.14311 (2021) - [i15]Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller:
Evaluating model-based planning and planner amortization for continuous control. CoRR abs/2110.03363 (2021) - [i14]Philemon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis:
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. CoRR abs/2111.00262 (2021) - [i13]Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell:
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies. CoRR abs/2112.05062 (2021) - 2020
- [j3]Josh Merel, Saran Tunyasuvunakool
Catch & Carry: reusable neural controllers for vision-guided whole-body tasks. ACM Trans. Graph. 39(4): 39 (2020) - [c8]Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller:
A distributional view on multi-objective policy optimization. ICML 2020: 11-22 - [c7]Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel:
CoMic: Complementary Task Learning & Mimicry for Reusable Skills. ICML 2020: 4105-4115 - [i12]Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber:
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. CoRR abs/2004.11410 (2020) - [i11]Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller:
A Distributional View on Multi-Objective Policy Optimization. CoRR abs/2005.07513 (2020) - [i10]Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess:
Importance Weighted Policy Learning and Adaption. CoRR abs/2009.04875 (2020) - [i9]Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess:
Behavior Priors for Efficient Reinforcement Learning. CoRR abs/2010.14274 (2020)
2010 – 2019
- 2019
- [c6]Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin:
Observational Learning by Reinforcement Learning. AAMAS 2019: 1117-1124 - [c5]Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess:
Information asymmetry in KL-regularized RL. ICLR (Poster) 2019 - [c4]Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess:
Neural Probabilistic Motor Primitives for Humanoid Control. ICLR (Poster) 2019 - [i8]Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess:
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. CoRR abs/1903.07438 (2019) - [i7]Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess:
Information asymmetry in KL-regularized RL. CoRR abs/1905.01240 (2019) - [i6]Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess:
Meta reinforcement learning as task inference. CoRR abs/1905.06424 (2019) - [i5]Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess:
Reusable neural skill embeddings for vision-guided whole body movement and object manipulation. CoRR abs/1911.06636 (2019) - 2018
- [b1]Leonard Hasenclever:
Probabilistic machine learning: methods and applications to continuous control. University of Oxford, UK, 2018 - [c3]Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu:
Mix & Match Agent Curricula for Reinforcement Learning. ICML 2018: 1095-1103 - [c2]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. UAI 2018: 393-402 - [i4]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. CoRR abs/1803.05649 (2018) - [i3]Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu:
Mix&Match - Agent Curricula for Reinforcement Learning. CoRR abs/1806.01780 (2018) - [i2]Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess:
Neural probabilistic motor primitives for humanoid control. CoRR abs/1811.11711 (2018) - 2017
- [j2]Leonard Hasenclever, Stefan Webb, Thibaut Liénart, Sebastian J. Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh:
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server. J. Mach. Learn. Res. 18: 106:1-106:37 (2017) - [c1]Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer:
Relativistic Monte Carlo. AISTATS 2017: 1236-1245 - 2015
- [i1]Yee Whye Teh, Leonard Hasenclever, Thibaut Liénart, Sebastian J. Vollmer, Stefan Webb, Balaji Lakshminarayanan, Charles Blundell:
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. CoRR abs/1512.09327 (2015) - 2014
- [j1]Wim Hordijk, Leonard Hasenclever, Jie Gao, Dilyana Mincheva, Jotun Hein:
An investigation into irreducible autocatalytic sets and power law distributed catalysis. Nat. Comput. 13(3): 287-296 (2014)
Coauthor Index
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last updated on 2024-11-13 23:53 CET by the dblp team
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