Fetching the paper…
Reading the bibliography…
We propose a novel framework for multi-task reinforcement learning (MTRL).
P. Dayan and G. E. Hinton, “Feudal reinforcement learning,” in Advances in neural information processing systems
1993
Earlier work this paper cites.
MIT Press, Cambridge, 1998
R. Sutton and A. Barto, Reinforcement learning · 1998
Earlier work this paper cites.
T. G. Dietterich, “The maxq method for hierarchical reinforcement learning,” in Proceedings of the Fifteenth International Conference on Machine Learning
1998
Earlier work this paper cites.
R. S. Sutton, D. Precup, and S. Singh, “Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,” Artificial intelligence
1999
Earlier work this paper cites.
H. J. Kappen, “Path integrals and symmetry breaking for optimal control theory,” Journal of statistical mechanics: theory and experiment
2005
Earlier work this paper cites.
E. Todorov, “General duality between optimal control and estimation,” in Decision and Control, 2008. CDC 2008. 47th IEEE Conference on
2008
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning.,” in AAAI
2008
Earlier work this paper cites.
M. E. Taylor and P. Stone, “Transfer learning for reinforcement learning domains: A survey,” Journal of Machine Learning Research
2009
Earlier work this paper cites.
J. Peters, K. Mülling, and Y. Altun, “Relative entropy policy search.,” in AAAI
2010
Earlier work this paper cites.
S. Levine and V. Koltun, “Variational policy search via trajectory optimization,” in Advances in Neural Information Processing Systems
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
2013
Earlier work this paper cites.
M. P. Deisenroth, P. Englert, J. Peters, and D. Fox, “Multi-task policy search for robotics,” in 2014 IEEE International Conference on Robotics and Automation (ICRA)
2014
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al
2015
Cited alongside, same era.
J. Grau-Moya, F. Leibfried, T. Genewein, and D. A. Braun, “Planning with information-processing constraints and model uncertainty in markov decision processes,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2016
Cited alongside, same era.
H. P. van Hasselt, A. Guez, M. Hessel, V. Mnih, and D. Silver, “Learning values across many orders of magnitude,” in Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Sæmundsson, K. Hofmann, and M. P. Deisenroth, “Meta reinforcement learning with latent variable gaussian processes,” May 2018
2018
Later among the works it cites.
A. Zhang, H. Satija, and J. Pineau, “Decoupling dynamics and reward for transfer learning,” 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
T. Haarnoja, H. Tang, P. Abbeel, and S. Levine, “Reinforcement learning with deep energy-based policies,” in International Conference on Machine Learning
2017
Cited alongside, same era.
J. Oh, S. Singh, H. Lee, and P. Kohli, “Zero-shot task generalization with multi-task deep reinforcement learning,” in International Conference on Machine Learning
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Devin, A. Gupta, T. Darrell, P. Abbeel, and S. Levine, “Learning modular neural network policies for multi-task and multi-robot transfer,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on
2017
Cited alongside, same era.
Y. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu, “Distral: Robust multitask reinforcement learning,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
2018
Later among the works it cites.
K. Hausman, J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller, “Learning an embedding space for transferable robot skills,” in International Conference on Learning Representations
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.