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Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods.
Evolutionary Principles in Self-referential Learning: On Learning how to Learn: the Meta-meta-meta…-hook
Schmidhuber, J · 1987
Earlier work this paper cites.
Learning internal representations
Baxter, J · 1995
Earlier work this paper cites.
Learning to Learn
Thrun, S. and Pratt, L. (eds.) · 1998
Earlier work this paper cites.
Learning to learn using gradient descent
Hochreiter, S., Younger, A., and Conwell, P · 2001
Earlier work this paper cites.
Curl: Contrastive unsupervised representations for reinforcement learning
Laskin, M., Srinivas, A., and Abbeel, P · 2004
Earlier work this paper cites.
Visualizing data using t-SNE
van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Discriminative unsupervised feature learning with exemplar convolutional neural networks, 2015
Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., and Brox, T · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent, 2016
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and de Freitas, N · 2016
Earlier work this paper cites.
Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
Earlier work this paper cites.
Learning to optimize, 2016
Li, K. and Malik, J · 2016
Earlier work this paper cites.
One-shot generalization in deep generative models, 2016
Rezende, D. J., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Earlier work this paper cites.
Learning to reinforcement learn, 2016
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M · 2016
Earlier work this paper cites.
Towards a neural statistician, 2017
Edwards, H. and Storkey, A · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks, 2017
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
A simple neural attentive meta-learner, 2017
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2017
Cited alongside, same era.
Loss is its own reward: Self-supervision for reinforcement learning, 2017
Shelhamer, E., Mahmoudieh, P., Argus, M., and Darrell, T · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning, 2017
Snell, J., Swersky, K., and Zemel, R. S · 2017
Cited alongside, same era.
Matching networks for one shot learning, 2017
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D · 2017
Cited alongside, same era.
Soft actor-critic algorithms and applications, 2019
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., and Levine, S · 2019
Later among the works it cites.
Human-level performance in 3d multiplayer games with population-based reinforcement learning
Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., and et al · 2019
Later among the works it cites.
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning, 2019
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., and Finn, C · 2019
Later among the works it cites.
Efficient off-policy meta-reinforcement learning via probabilistic context variables, 2019
Rakelly, K., Zhou, A., Quillen, D., Finn, C., and Levine, S · 2019
Later among the works it cites.
Some considerations on learning to explore via meta-reinforcement learning, 2019
Stadie, B. C., Yang, G., Houthooft, R., Chen, X., Duan, Y., Wu, Y., Abbeel, P., and Sutskever, I · 2019
Later among the works it cites.
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Openai five
OpenAI · 2018
Cited alongside, same era.
Promp: Proximal meta-policy search, 2018
Rothfuss, J., Lee, D., Clavera, I., Asfour, T., and Abbeel, P · 2018
Cited alongside, same era.
Time-contrastive networks: Self-supervised learning from video, 2018
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., and Levine, S · 2018
Cited alongside, same era.
Meta reinforcement learning with latent variable gaussian processes, 2018
Sæmundsson, S., Hofmann, K., and Deisenroth, M. P · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance-level discrimination, 2018
Wu, Z., Xiong, Y., Yu, S., and Lin, D · 2018
Cited alongside, same era.
Learning dexterous in-hand manipulation
Andrychowicz, O. M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations, 2020
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Later among the works it cites.
Exploring simple siamese representation learning, 2020
Chen, X. and He, K · 2020
Later among the works it cites.
Meta-q-learning, 2020
Fakoor, R., Chaudhari, P., Soatto, S., and Smola, A. J · 2020
Later among the works it cites.
Towards effective context for meta-reinforcement learning: an approach based on contrastive learning
Fu, H., Tang, H., Hao, J., Chen, C., Feng, X., Li, D., and Liu, W · 2020
Later among the works it cites.
Bootstrap your own latent: A new approach to self-supervised learning, 2020
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning, 2020
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding, 2020
Hénaff, O. J., Srinivas, A., Fauw, J. D., Razavi, A., Doersch, C., Eslami, S. M. A., and van den Oord, A · 2020
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2020
Later among the works it cites.