Dhruva Tirumala
Dhruva Tirumala
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Cited by
Emergence of locomotion behaviours in rich environments
N Heess, D TB, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ...
arXiv preprint arXiv:1707.02286, 2017
Learning to reinforcement learn
JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ...
arXiv preprint arXiv:1611.05763, 2016
Prefrontal cortex as a meta-reinforcement learning system
JX Wang, Z Kurth-Nelson, D Kumaran, D Tirumala, H Soyer, JZ Leibo, ...
Nature neuroscience 21 (6), 860-868, 2018
Distributed distributional deterministic policy gradients
G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ...
arXiv preprint arXiv:1804.08617, 2018
Learning human behaviors from motion capture by adversarial imitation
J Merel, Y Tassa, D TB, S Srinivasan, J Lemmon, Z Wang, G Wayne, ...
arXiv preprint arXiv:1707.02201, 2017
Hierarchical visuomotor control of humanoids
J Merel, A Ahuja, V Pham, S Tunyasuvunakool, S Liu, D Tirumala, ...
arXiv preprint arXiv:1811.09656, 2018
Information asymmetry in KL-regularized RL
A Galashov, SM Jayakumar, L Hasenclever, D Tirumala, J Schwarz, ...
arXiv preprint arXiv:1905.01240, 2019
V-mpo: On-policy maximum a posteriori policy optimization for discrete and continuous control
HF Song, A Abdolmaleki, JT Springenberg, A Clark, H Soyer, JW Rae, ...
arXiv preprint arXiv:1909.12238, 2019
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
Learning to reinforcement learn. ArXiv 1611.05763
JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ...
Probing physics knowledge using tools from developmental psychology
L Piloto, A Weinstein, D TB, A Ahuja, M Mirza, G Wayne, D Amos, C Hung, ...
arXiv preprint arXiv:1804.01128, 2018
Data-efficient hindsight off-policy option learning
M Wulfmeier, D Rao, R Hafner, T Lampe, A Abdolmaleki, T Hertweck, ...
International Conference on Machine Learning, 11340-11350, 2021
Behavior priors for efficient reinforcement learning
D Tirumala, A Galashov, H Noh, L Hasenclever, R Pascanu, J Schwarz, ...
arXiv preprint arXiv:2010.14274, 2020
Pick your battles: Interaction graphs as population-level objectives for strategic diversity
M Garnelo, WM Czarnecki, S Liu, D Tirumala, J Oh, G Gidel, ...
arXiv preprint arXiv:2110.04041, 2021
Learning to reinforcement learn (2016)
JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ...
arXiv preprint arXiv:1611.05763, 2016
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
Meta-reinforcement learning: a bridge between prefrontal and dopaminergic function
JX Wang, Z Kurth-Nelson, D Tirumala, J Leibo, H Soyer, D Kumaran, ...
Cosyne abstracts, 2017
Flexible support for fast parallel commutative updates
V Balaji, D Tirumala, B Lucia
arXiv preprint arXiv:1709.09491, 2017
Poster: An architecture and programming model for accelerating parallel commutative computations via privatization
V Balaji, D Tirumala, B Lucia
ACM SIGPLAN Notices 52 (8), 431-432, 2017
MO2: Model-Based Offline Options
S Salter, M Wulfmeier, D Tirumala, N Heess, M Riedmiller, R Hadsell, ...
arXiv preprint arXiv:2209.01947, 2022
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