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We focus on the problem of teaching a robot to solve tasks presented sequentially, i.e., in a continual learning scenario.
Monte-carlo tree search: A new framework for game ai
Chaslot, G., Bakkes, S., Szita, I., and Spronck, P · 2008
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TacTex’13: A champion adaptive power trading agent
Urieli, D. and Stone, P · 2014
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Deep spatial autoencoders for visuomotor learning
Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Rusu, A. A., Colmenarejo, S. G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Watter, M., Springenberg, J., Boedecker, J., and Riedmiller, M · 2015
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Transfer from simulation to real world through learning deep inverse dynamics model
Christiano, P., Shah, Z., Mordatch, I., Schneider, J., Blackwell, T., Tobin, J., Abbeel, P., and Zaremba, W · 2016
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Deep reinforcement learning for dialogue generation
Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., and Jurafsky, D · 2016
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Faster Teaching via POMDP Planning
Rafferty, A. N., Brunskill, E., Griffiths, T. L., and Shafto, P · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Stable reinforcement learning with autoencoders for tactile and visual data
van Hoof, H., Chen, N., Karl, M., van der Smagt, P., and Peters, J · 2016
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Hindsight experience replay
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, O. P., and Zaremba, W · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Unsupervised state representation learning with robotic priors: a robustness benchmark
Lesort, T., Seurin, M., Li, X., Díaz-Rodríguez, N., and Filliat, D · 2017
Cited alongside, same era.
Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
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Time-contrastive networks: Self-supervised learning from multi-view observation
Sermanet, P., Lynch, C., Hsu, J., and Levine, S · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J · 2017
Cited alongside, same era.
State representation learning for control: An overview
Lesort, T., Díaz-Rodríguez, N., Goudou, J.-F., and Filliat, D · 2018
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Marginal Replay vs Conditional Replay for Continual Learning
Lesort, T., Gepperth, A., Stoian, A., and Filliat, D · 2018
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Learning without forgetting
Li, Z. and Hoiem, D · 2018
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Continuous learning in single-incremental-task scenarios
Maltoni, D. and Lomonaco, V · 2018
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Sim-to-real reinforcement learning for deformable object manipulation
Matas, J., James, S., and Davison, A. J · 2018
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Openai five
OpenAI · 2018
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Distral: Robust multitask reinforcement learning
Teh, Y., Bapst, V., Czarnecki, W. M., Quan, J., Kirkpatrick, J., Hadsell, R., Heess, N., and Pascanu, R · 2017
Cited alongside, same era.
Independently controllable factors
Thomas, V., Pondard, J., Bengio, E., Sarfati, M., Beaudoin, P., Meurs, M., Pineau, J., Precup, D., and Bengio, Y · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Cited alongside, same era.
Life-long disentangled representation learning with cross-domain latent homologies
Achille, A., Eccles, T., Matthey, L., Burgess, C., Watters, N., Lerchner, A., and Higgins, I · 2018
Cited alongside, same era.
CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning
Colas, C., Sigaud, O., and Oudeyer, P.-Y · 2018
Cited alongside, same era.
Open-ended learning: a conceptual framework based on representational redescription
Doncieux, S., Filliat, D., Díaz-Rodríguez, N., Hospedales, T., Duro, R., Coninx, A., Roijers, D. M., Girard, B., Perrin, N., and Sigaud, O · 2018
Cited alongside, same era.
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S-RL toolbox: Environments, datasets and evaluation metrics for state representation learning
Raffin, A., Hill, A., Traoré, R., Lesort, T., Díaz-Rodríguez, N., and Filliat, D · 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
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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
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 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
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S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
Caselles-Dupré, H., Garcia-Ortiz, M., and Filliat, D · 2019
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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
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AlphaStar blog post , 2019
Vinyals, O., Babuschkin, I., and Chung, J · 2019
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