Fetching the paper…
Reading the bibliography…
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.
Finding structure in reinforcement learning
S. Thrun and A. Schwartz · 1995
Earlier work this paper cites.
The MAXQ method for hierarchical reinforcement learning
T. G. Dietterich · 1998
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
A. Y. Ng, D. Harada, and S. Russell · 1999
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
R. S. Sutton, D. Precup, and S. Singh · 1999
Earlier work this paper cites.
Automatic discovery of subgoals in reinforcement learning using diverse density
A. McGovern and A. G. Barto · 2001
Earlier work this paper cites.
The utility of temporal abstraction in reinforcement learning
N. K. Jong, T. Hester, and P. Stone · 2008
Earlier work this paper cites.
General duality between optimal control and estimation
E. Todorov · 2008
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective
M. M. Botvinick, Y. Niv, and A. C. Barto · 2009
Earlier work this paper cites.
Hierarchical relative entropy policy search
C. Daniel, G. Neumann, and J. Peters · 2012
Earlier work this paper cites.
Probabilistic inference for determining options in reinforcement learning
C. Daniel, H. Van Hoof, J. Peters, and G. Neumann · 2016
Earlier work this paper cites.
Principled option learning in markov decision processes
R. Fox, M. Moshkovitz, and N. Tishby · 2016
Cited alongside, same era.
K. Gregor, D. J. Rezende, and D. Wierstra · 2016
Cited alongside, same era.
Learning and transfer of modulated locomotor controllers
N. Heess, G. Wayne, Y. Tassa, T. Lillicrap, M. Riedmiller, and D. Silver · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Learning to reinforcement learn
J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2016
Feudal networks for hierarchical reinforcement learning
A. S. Vezhnevets, S. Osindero, T. Schaul, N. Heess, M. Jaderberg, D. Silver, and K. Kavukcuoglu · 2017
Later among the works it cites.
Meta learning shared hierarchies
K. Frans, J. Ho, X. Chen, P. Abbeel, and J. Schulman · 2018
Later among the works it cites.
Latent space policies for hierarchical reinforcement learning
T. Haarnoja, K. Hartikainen, P. Abbeel, and S. Levine · 2018
Later among the works it cites.
Learning an embedding space for transferable robot skills
K. Hausman, J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller · 2018
Later among the works it cites.
Reinforcement learning and control as probabilistic inference: Tutorial and review
S. Levine · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Modular multitask reinforcement learning with policy sketches
J. Andreas, D. Klein, and S. Levine · 2017
Cited alongside, same era.
The option-critic architecture
P.-L. Bacon, J. Harb, and D. Precup · 2017
Cited alongside, same era.
When waiting is not an option: Learning options with a deliberation cost
J. Harb, P.-L. Bacon, M. Klissarov, and D. Precup · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Cited alongside, same era.
Distral: Robust multitask reinforcement learning
Y. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu · 2017
Cited alongside, same era.
A deep hierarchical approach to lifelong learning in minecraft
C. Tessler, S. Givony, T. Zahavy, D. J. Mankowitz, and S. Mannor · 2017
Cited alongside, same era.
O. Nachum, S. Gu, H. Lee, and S. Levine · 2018
Later among the works it cites.
Information asymmetry in KL-regularized RL
A. Galashov, S. Jayakumar, L. Hasenclever, D. Tirumala, J. Schwarz, G. Desjardins, W. M. Czarnecki, Y. W. Teh, R. Pascanu, and N. Heess · 2019
Closest in time.
Transfer and exploration via the information bottleneck
A. Goyal, R. Islam, D. Strouse, Z. Ahmed, H. Larochelle, M. Botvinick, S. Levine, and Y. Bengio · 2019
Closest in time.
A. Harutyunyan, W. Dabney, D. Borsa, N. Heess, R. Munos, and D. Precup · 2019
Closest in time.
Exploiting hierarchy for learning and transfer in kl-regularized rl
D. Tirumala, H. Noh, A. Galashov, L. Hasenclever, A. Ahuja, G. Wayne, R. Pascanu, Y. W. Teh, and N. Heess · 2019
Closest in time.