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In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning.
Reinforcement learning with augmented data
Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., and Srinivas, A · 2004
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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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., et al · 2015
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Beattie, C., Leibo, J. Z., Teplyashin, D., Ward, T., Wainwright, M., Küttler, H., Lefrancq, A., Green, S., Valdés, V., Sadik, A., Schrittwieser, J., Anderson, K., York, S., Cant, M., Cain, A., Bolton, A., Gaffney, S., King, H., Hassabis, D., Legg, S., and Petersen, S · 2016
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Deep spatial autoencoders for visuomotor learning
Finn, C., Xin Yu Tan, Yan Duan, Darrell, T., Levine, S., and Abbeel, P · 2016
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Carla: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Deep object-centric representations for generalizable robot learning
Devin, C., Abbeel, P., Darrell, T., and Levine, S · 2018
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Neural predictive belief representations
Guo, Z. D., Azar, M. G., Piot, B., Pires, B. A., and Munos, R · 2018
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Ha, D. and Schmidhuber, J · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., Modayil, J., van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2018
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al · 2018
Multi-task deep reinforcement learning with popart
Hessel, M., Soyer, H., Espeholt, L., Czarnecki, W., Schmitt, S., and van Hasselt, H · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castaneda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al · 2019
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Contrastive learning of structured world models
Kipf, T., van der Pol, E., and Welling, M · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Lee, A. X., Nagabandi, A., Abbeel, P., and Levine, S · 2019
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A simple framework for contrastive learning of visual representations
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Cited alongside, same era.
Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Unsupervised predictive memory in a goal-directed agent
Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska-Barwinska, A., Rae, J., Mirowski, P., Leibo, J. Z., Santoro, A., et al · 2018
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Unsupervised state representation learning in atari
Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., and Hjelm, R. D · 2019
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
Cited alongside, same era.
Leveraging procedural generation to benchmark reinforcement learning
Cobbe, K., Hesse, C., Hilton, J., and Schulman, J · 2019
Cited alongside, same era.
Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2019
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
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., et al · 2020
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Bootstrap latent-predictive representations for multitask reinforcement learning
Guo, D., Pires, B. A., Piot, B., Grill, J.-b., Altché, F., Munos, R., and Azar, M. G · 2020
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Dream to control: Learning behaviors by latent imagination
Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Kostrikov, I., Yarats, D., and Fergus, R · 2020
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Predictive information accelerates learning in rl
Lee, K.-H., Fischer, I., Liu, A., Guo, Y., Lee, H., Canny, J., and Guadarrama, S · 2020
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Deep reinforcement and infomax learning
Mazoure, B., Combes, R. T. d., Doan, T., Bachman, P., and Hjelm, R. D · 2020
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Data-efficient reinforcement learning with momentum predictive representations
Schwarzer, M., Anand, A., Goel, R., Hjelm, R. D., Courville, A., and Bachman, P · 2020
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Learning predictive representations for deformable objects using contrastive estimation
Yan, W., Vangipuram, A., Abbeel, P., and Pinto, L · 2020
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