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The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to improve performance on any single task.
Roberta: A robustly optimized bert pretraining approach
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The probable error of a mean
Student · 1908
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Dynamic Programming
Bellman, R · 1957
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Asymptotically efficient adaptive allocation rules
Lai, T. and Robbins, H · 1985
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Markov decision processes: Discrete stochastic dynamic programming
Puterman, M. L · 1995
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Multitask learning
Caruana, R · 1997
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Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R · 1998
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Gradient surgery for multi-task learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C · 2001
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Multitask reinforcement learning on the distribution of MDPs
Tanaka, F. and Yamamura, M · 2003
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The epoch-greedy algorithm for multi-armed bandits with side information
Langford, J. and Zhang, T · 2007
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Towards grounding concepts for transfer in goal learning from demonstration
Chao, C., Cakmak, M., and Thomaz, A. L · 2011
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Learning to interpret natural language navigation instructions from observations
Chen, D. L. and Mooney, R. J · 2011
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Understanding natural language commands for robotic navigation and mobile manipulation
Tellex, S., Kollar, T., Dickerson, S., Walter, M., Banerjee, A., Teller, S., and Roy, N · 2011
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Playing atari with deep reinforcement learning, 2013
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Sparse multi-task reinforcement learning
Calandriello, D., Lazaric, A., and Restelli, M · 2014
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Facial landmark detection by deep multi-task learning
Zhang, Z., Luo, P., Loy, C. C., and Tang, X · 2014
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Contextual markov decision processes, 2015
Hallak, A., Castro, D. D., and Mannor, S · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
Parisotto, E., Ba, J. L., and Salakhutdinov, R · 2015
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Learning shared representations in multi-task reinforcement learning
Borsa, D., Graepel, T., and Shawe-Taylor, J · 2016
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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The benefit of multitask representation learning
Maurer, A., Pontil, M., and Romera-Paredes, B · 2016
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Epopt: Learning robust neural network policies using model ensembles
Rajeswaran, A., Ghotra, S., Ravindran, B., and Levine, S · 2016
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Rusu, A. A., Colmenarejo, S. G., Çaglar Gülçehre, Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R · 2016
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Metadata-based clustered multi-task learning for thread mining in web communities
You, Q., Wu, O., Luo, G., and Hu, W · 2016
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Modular multitask reinforcement learning with policy sketches
Andreas, J., Klein, D., and Levine, S · 2017
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Gated-attention architectures for task-oriented language grounding
Chaplot, D. S., Sathyendra, K. M., Pasumarthi, R. K., Rajagopal, D., and Salakhutdinov, R · 2017
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Learning modular neural network policies for multi-task and multi-robot transfer
Devin, C., Gupta, A., Darrell, T., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Scalable multitask policy gradient reinforcement learning
El Bsat, S., Bou-Ammar, H., and Taylor, M. E · 2017
Cited alongside, same era.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2017
Cited alongside, same era.
Grounded language learning in a simulated 3d world
Hermann, K. M., Hill, F., Green, S., Wang, F., Faulkner, R., Soyer, H., Szepesvari, D., Czarnecki, W. M., Jaderberg, M., Teplyashin, D., et al · 2017
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Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I · 2017
A survey of reinforcement learning informed by natural language
Luketina, J., Nardelli, N., Farquhar, G., Foerster, J., Andreas, J., Grefenstette, E., Whiteson, S., and Rocktäschel, T · 2019
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Towards interpretable reinforcement learning using attention augmented agents
Mott, A., Zoran, D., Chrzanowski, M., Wierstra, D., and Rezende, D. J · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Pathak, D., Lu, C., Darrell, T., Isola, P., and Efros, A. A · 2019
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Language models are unsupervised multitask learners
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Cited alongside, same era.
Markov Decision Processes with Continuous Side Information
Modi, A., Jiang, N., Singh, S., and Tewari, A · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Ruder, S · 2017
Cited alongside, same era.
Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
Shu, T., Xiong, C., and Socher, R · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., and Hassabis, D · 2017
Cited alongside, same era.
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.
Automatically composing representation transformations as a means for generalization
Chang, M. B., Gupta, A., Levine, S., and Griffiths, T. L · 2018
Cited alongside, same era.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A · 2018
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Which tasks should be learned together in multi-task learning?
Standley, T., Zamir, A. R., Chen, D., Guibas, L., Malik, J., and Savarese, S · 2019
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Regularizing deep multi-task networks using orthogonal gradients
Suteu, M. and Guo, Y · 2019
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Hydra - a framework for elegantly configuring complex applications
Yadan, O · 2019
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Learning causal state representations of partially observable environments, 2019
Zhang, A., Lipton, Z. C., Pineda, L., Azizzadenesheli, K., Anandkumar, A., Itti, L., Pineau, J., and Furlanello, T · 2019
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Metadata-driven task relation discovery for multi-task learning
Zheng, Z., Wang, Y., Dai, Q., Zheng, H., and Wang, D · 2019
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Allen, K. R., Smith, K. A., and Tenenbaum, J. B · 2020
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Sharing knowledge in multi-task deep reinforcement learning
D’Eramo, C., Tateo, D., Bonarini, A., Restelli, M., and Peters, J · 2020
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Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del R’ıo, J. F., Wiebe, M., Peterson, P., G’erard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E · 2020
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Multitask soft option learning, 2020
Igl, M., Gambardella, A., He, J., Nardelli, N., Siddharth, N., Böhmer, W., and Whiteson, S · 2020
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Causal discovery in physical systems from videos, 2020
Li, Y., Torralba, A., Anandkumar, A., Fox, D., and Garg, A · 2020
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Object-centric learning with slot attention
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T · 2020
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Intervention design for effective sim2real transfer
Mozifian, M., Zhang, A., Pineau, J., and Meger, D · 2020
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pandas-dev/pandas: Pandas, February 2020
pandas development team, T · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Srinivas, A., Laskin, M., and Abbeel, P · 2020
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Multi-task reinforcement learning with soft modularization
Yang, R., Xu, H., Wu, Y., and Wang, X · 2020
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Invariant causal prediction for block MDPs
Zhang, A., Lyle, C., Sodhani, S., Filos, A., Kwiatkowska, M., Pineau, J., Gal, Y., and Precup, D · 2020
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Rtfm: Generalising to new environment dynamics via reading
Zhong, V., Rocktäschel, T., and Grefenstette, E · 2020
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Mtrl - multi task rl algorithms
Sodhani, S. and Zhang, A · 2021
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Mtenv - environment interface for mulit-task reinforcement learning
Sodhani, S., Denoyer, L., Kamienny, P.-A., and Delalleau, O · 2021
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Learning robust state abstractions for hidden-parameter block MDPs
Zhang, A., Sodhani, S., Khetarpal, K., and Pineau, J · 2021
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A survey of multi-task deep reinforcement learning
Vithayathil Varghese, N. and Mahmoud, Q. H · 2079
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