Leonard Hasenclever
Leonard Hasenclever
Research Scientist at DeepMind
Verified email at - Homepage
Cited by
Cited by
Sylvester Normalizing Flows for Variational Inference
R Berg, L Hasenclever, JM Tomczak, M Welling
UAI, 2018
Neural probabilistic motor primitives for humanoid control
J Merel, L Hasenclever, A Galashov, A Ahuja, V Pham, G Wayne, YW Teh, ...
arXiv preprint arXiv:1811.11711, 2018
Meta reinforcement learning as task inference
J Humplik, A Galashov, L Hasenclever, PA Ortega, YW Teh, N Heess
arXiv preprint arXiv:1905.06424, 2019
Catch & carry: reusable neural controllers for vision-guided whole-body tasks
J Merel, S Tunyasuvunakool, A Ahuja, Y Tassa, L Hasenclever, V Pham, ...
ACM Transactions on Graphics (TOG) 39 (4), 39: 1-39: 12, 2020
Information asymmetry in KL-regularized RL
A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ...
International Conference on Learning Representations, 2018
From motor control to team play in simulated humanoid football
S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ...
Science Robotics 7 (69), eabo0235, 2022
Mix & match agent curricula for reinforcement learning
W Czarnecki, S Jayakumar, M Jaderberg, L Hasenclever, YW Teh, ...
International Conference on Machine Learning, 1087-1095, 2018
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ...
The Journal of Machine Learning Research 18 (1), 3744-3780, 2017
A distributional view on multi-objective policy optimization
A Abdolmaleki, S Huang, L Hasenclever, M Neunert, F Song, M Zambelli, ...
International conference on machine learning, 11-22, 2020
Observational learning by reinforcement learning
D Borsa, B Piot, R Munos, O Pietquin
arXiv preprint arXiv:1706.06617, 2017
The true cost of stochastic gradient Langevin dynamics
T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ...
arXiv preprint arXiv:1706.02692, 2017
Relativistic Monte Carlo
X Lu, V Perrone, L Hasenclever, YW Teh, SJ Vollmer
Exploiting hierarchy for learning and transfer in kl-regularized rl
D Tirumala, H Noh, A Galashov, L Hasenclever, A Ahuja, G Wayne, ...
arXiv preprint arXiv:1903.07438, 2019
CoMic: Complementary task learning & mimicry for reusable skills
L Hasenclever, F Pardo, R Hadsell, N Heess, J Merel
International Conference on Machine Learning, 4105-4115, 2020
Lateral controls on grounding-line dynamics
SS Pegler, KN Kowal, LQ Hasenclever, MG Worster
Journal of Fluid Mechanics 722, R1, 2013
Behavior priors for efficient reinforcement learning
D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ...
The Journal of Machine Learning Research 23 (1), 9989-10056, 2022
Divide-and-conquer monte carlo tree search for goal-directed planning
G Parascandolo, L Buesing, J Merel, L Hasenclever, J Aslanides, ...
arXiv preprint arXiv:2004.11410, 2020
An investigation into irreducible autocatalytic sets and power law distributed catalysis
W Hordijk, L Hasenclever, J Gao, D Mincheva, J Hein
Natural Computing 13, 287-296, 2014
Learning dynamics models for model predictive agents
M Lutter, L Hasenclever, A Byravan, G Dulac-Arnold, P Trochim, N Heess, ...
arXiv preprint arXiv:2109.14311, 2021
Learning transferable motor skills with hierarchical latent mixture policies
D Rao, F Sadeghi, L Hasenclever, M Wulfmeier, M Zambelli, G Vezzani, ...
arXiv preprint arXiv:2112.05062, 2021
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