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Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).
Training and tracking in robotics
O. G. Selfridge, R. S. Sutton, and A. G. Barto · 1985
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Curious model-building control systems
J. Schmidhuber · 1991
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Transfer of learning by composing solutions of elemental sequential tasks
S. P. Singh · 1992
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Policy invariance under reward transformations: Theory and application to reward shaping
A. Y. Ng, D. Harada, and S. J. Russell · 1999
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Multi-task reinforcement learning: a hierarchical bayesian approach
A. Wilson, A. Fern, S. Ray, and P. Tadepalli · 2007
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Hippocampal contributions to control: the third way
M. Lengyel and P. Dayan · 2007
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Flexible shaping: How learning in small steps helps
K. A. Krueger and P. Dayan · 2009
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
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A regularization approach to learning task relationships in multitask learning
Y. Zhang and D.-Y. Yeung · 2014
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Actor-mimic: Deep multitask and transfer reinforcement learning
E. Parisotto, J. L. Ba, and R. Salakhutdinov · 2015
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A. A. Rusu, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell · 2015
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Universal value function approximators
T. Schaul, D. Horgan, K. Gregor, and D. Silver · 2015
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S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus · 2015
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Unifying count-based exploration and intrinsic motivation
M. G. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos · 2016
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Source task creation for curriculum learning
S. Narvekar, J. Sinapov, M. Leonetti, and P. Stone · 2016
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C. Blundell, B. Uria, A. Pritzel, Y. Li, A. Ruderman, J. Z. Leibo, J. Rae, D. Wierstra, and D. Hassabis · 2016
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A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
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Dynamic filter networks
X. Jia, B. De Brabandere, T. Tuytelaars, and L. Van Gool · 2016
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Reverse curriculum generation for reinforcement learning
C. Florensa, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel · 2017
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Hypernetworks
D. Ha, A. M. Dai, and Q. V. Le · 2017
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Intrinsic motivation and automatic curricula via asymmetric self-play
S. Sukhbaatar, Z. Lin, I. Kostrikov, G. Synnaeve, A. Szlam, and R. Fergus · 2017
Cited alongside, same era.
Autonomous task sequencing for customized curriculum design in reinforcement learning
S. Narvekar, J. Sinapov, and P. Stone · 2017
Cited alongside, same era.
Automatic curriculum graph generation for reinforcement learning agents
M. Svetlik, M. Leonetti, J. Sinapov, R. Shah, N. Walker, and P. Stone · 2017
Cited alongside, same era.
Automated curriculum learning for neural networks
A. Graves, M. G. Bellemare, J. Menick, R. Munos, and K. Kavukcuoglu · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
Cited alongside, same era.
Language grounding through social interactions and curiosity-driven multi-goal learning
N. Lair, C. Colas, R. Portelas, J.-M. Dussoux, P. F. Dominey, and P.-Y. Oudeyer · 2019
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Model-based active exploration
P. Shyam, W. Jaśkowski, and F. Gomez · 2019
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Adaptive auxiliary task weighting for reinforcement learning
X. Lin, H. S. Baweja, G. Kantor, and D. Held · 2019
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Composing task-agnostic policies with deep reinforcement learning
A. H. Qureshi, J. J. Johnson, Y. Qin, T. Henderson, B. Boots, and M. C. Yip · 2019
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Mcp: Learning composable hierarchical control with multiplicative compositional policies
X. B. Peng, M. Chang, G. Zhang, P. Abbeel, and S. Levine · 2019
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L. Pinto and A. Gupta · 2017
Cited alongside, same era.
Distral: Robust multitask reinforcement learning
Y. W. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu · 2017
Cited alongside, same era.
Modular multitask reinforcement learning with policy sketches
J. Andreas, D. Klein, and S. Levine · 2017
Cited alongside, same era.
H. Sahni, S. Kumar, F. Tejani, and C. Isbell · 2017
Cited alongside, same era.
Neural episodic control
A. Pritzel, B. Uria, S. Srinivasan, A. P. Badia, O. Vinyals, D. Hassabis, D. Wierstra, and C. Blundell · 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.
Rainbow: Combining improvements in deep reinforcement learning
M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. G. Azar, and D. Silver · 2018
Cited alongside, same era.
Generalized inner loop meta-learning
E. Grefenstette, B. Amos, D. Yarats, P. M. Htut, A. Molchanov, F. Meier, D. Kiela, K. Cho, and S. Chintala · 2019
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Teacher–student curriculum learning
T. Matiisen, A. Oliver, T. Cohen, and J. Schulman · 2019
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Barc: Backward reachability curriculum for robotic reinforcement learning
B. Ivanovic, J. Harrison, A. Sharma, M. Chen, and M. Pavone · 2019
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Principled weight initialization for hypernetworks
O. Chang, L. Flokas, and H. Lipson · 2019
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Automatic curriculum learning for deep rl: A short survey
R. Portelas, C. Colas, L. Weng, K. Hofmann, and P.-Y. Oudeyer · 2020
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Gradient surgery for multi-task learning
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn · 2020
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Multi-task reinforcement learning with soft modularization
R. Yang, H. Xu, Y. Wu, and X. Wang · 2020
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Multi-agent reinforcement learning with emergent roles
T. Wang, H. Dong, V. Lesser, and C. Zhang · 2020
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Learning with amigo: Adversarially motivated intrinsic goals
A. Campero, R. Raileanu, H. Küttler, J. B. Tenenbaum, T. Rocktäschel, and E. Grefenstette · 2020
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Evolutionary population curriculum for scaling multi-agent reinforcement learning
Q. Long, Z. Zhou, A. Gupta, F. Fang, Y. Wu, and X. Wang · 2020
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Meta automatic curriculum learning
R. Portelas, C. Romac, K. Hofmann, and P.-Y. Oudeyer · 2020
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On the modularity of hypernetworks
T. Galanti and L. Wolf · 2020
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Dynamic neural networks: A survey
Y. Han, G. Huang, S. Song, L. Yang, H. Wang, and Y. Wang · 2021
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
O. Ahmed, F. Träuble, A. Goyal, A. Neitz, M. Wuthrich, Y. Bengio, B. Schölkopf, and S. Bauer · 2021
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A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27
Y. LeCun · 2022
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Coordination among neural modules through a shared global workspace
A. Goyal, A. R. Didolkar, A. Lamb, K. Badola, N. R. Ke, N. Rahaman, J. Binas, C. Blundell, M. C. Mozer, and Y. Bengio · 2022
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