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In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal.
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
Caselles-Dupré, H., Garcia-Ortiz, M., and Filliat, D. (2019) · 1902
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Continual reinforcement learning in 3d non-stationary environments
Lomonaco, V., Desai, K., Culurciello, E., and Maltoni, D. (2019) · 1905
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Continual Learning for Robotics
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Díaz-Rodríguez, N. (2019) · 1907
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Multitask learning
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Pathnet: Evolution channels gradient descent in super neural networks
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Continual learning with deep generative replay
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Domain randomization for transferring deep neural networks from simulation to the real world
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State representation learning for control: An overview
Lesort, T., Díaz-Rodríguez, N., Goudou, J.-F., and Filliat, D. (2018) · 2018
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Marginal Replay vs Conditional Replay for Continual Learning
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Learning without forgetting
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Continuous learning in single-incremental-task scenarios
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Sim-to-real reinforcement learning for deformable object manipulation
Matas, J., James, S., and Davison, A. J. (2018) · 2018
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Life-long disentangled representation learning with cross-domain latent homologies
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Furlanello, T., Lipton, Z. C., Tschannen, M., Itti, L., and Anandkumar, A. (2018) · 2018
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How to Train Your Robot - New Environments for Robotic Training and New Methods for Transferring Policies from the Simulator to the Real Robot
Golemo, F. (2018) · 2018
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Generative Models from the perspective of Continual Learning
Lesort, T., Caselles-Dupré, H., Garcia- Ortiz, M., Stoian, A., and Filliat, D. (2018) · 2018
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016a)
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Raffin, A., Hill, A., Traoré, R., Lesort, T., Díaz-Rodríguez, N., and Filliat, D. (2018) · 2018
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Learning by playing-solving sparse reward tasks from scratch
Riedmiller, M., Hafner, R., Lampe, T., Neunert, M., Degrave, J., Van de Wiele, T., Mnih, V., Heess, N., and Springenberg, J. T. (2018) · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R. (2018) · 2018
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Memory replay gans: learning to generate images from new categories without forgetting
Wu, C., Herranz, L., Liu, X., Wang, Y., van de Weijer, J., and Raducanu, B. (2018) · 2018
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Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Raffin, A., Hill, A., Traoré, K. R., Lesort, T., Díaz-Rodríguez, N., and Filliat, D. (2019) · 2019
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