Hasenclever, Leonard

17 publications

CoLLAs 2024 Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning Norman Di Palo, Leonard Hasenclever, Jan Humplik, Arunkumar Byravan

CoRL 2024 Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin Riedmiller, Nicolas Heess

ICLR 2024 Replay Across Experiments: A Natural Extension of Off-Policy RL Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin Riedmiller, Nicolas Heess, Markus Wulfmeier

CoRL 2023 Language to Rewards for Robotic Skill Synthesis 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

ICLRW 2023 Towards a Unified Agent with Foundation Models Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin Riedmiller

JMLR 2022 Behavior Priors for Efficient Reinforcement Learning Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

ICLR 2022 Evaluating Model-Based Planning and Planner Amortization for Continuous Control Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller

ICLR 2022 Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

NeurIPSW 2021 Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

ICML 2020 A Distributional View on Multi-Objective Policy Optimization Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller

ICML 2020 CoMic: Complementary Task Learning & Mimicry for Reusable Skills Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel

ICLR 2019 Information Asymmetry in KL-Regularized RL Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

ICLR 2019 Neural Probabilistic Motor Primitives for Humanoid Control Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

ICML 2018 Mix & Match Agent Curricula for Reinforcement Learning Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

UAI 2018 Sylvester Normalizing Flows for Variational Inference Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

JMLR 2017 Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh

AISTATS 2017 Relativistic Monte Carlo Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever, Yee Whye Teh, Sebastian J. Vollmer