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Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks.
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. A · 2013
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State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction
Jonschkowski, R. and Brock, O · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Learning state representations with robotic priors
Jonschkowski, R. and Brock, O · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Watter, M., Springenberg, J. T., Boedecker, J., and Riedmiller, M · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Wide & Deep Learning for Recommender Systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., and Shah, H · 2016
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Benchmarking Deep Reinforcement Learning for Continuous Control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P · 2016
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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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 · 2016
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Learning state representation for deep actor-critic control
Munk, J., Kober, J., and Babuška, R · 2016
Cited alongside, same era.
Safe and efficient off-policy reinforcement learning
Munos, R., Stepleton, T., Harutyunyan, A., and Bellemare, M · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Autoencoder-augmented neuroevolution for visual doom playing
Alvernaz, S. and Togelius, J · 2017
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., and Levine, S · 2017
Cited alongside, same era.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Buckman, J., Hafner, D., Tucker, G., Brevdo, E., and Lee, H · 2018
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Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., and Meger, D · 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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Deep Reinforcement Learning That Matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2018
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Guo, H., Tang, R., Ye, Y., Li, Z., and He, X · 2017
Cited alongside, same era.
Densely Connected Convolutional Networks
Huang, G., Liu, Z., v. d. Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Uncertainty-driven imagination for continuous deep reinforcement learning
Kalweit, G. and Boedecker, J · 2017
Cited alongside, same era.
Self-Normalizing Neural Networks
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S · 2017
Cited alongside, same era.
The Expressive Power of Neural Networks: A View from the Width
Lu, Z., Pu, H., Wang, F., Hu, Z., and Wang, L · 2017
Cited alongside, same era.
Towards generalization and simplicity in continuous control
Rajeswaran, A., Lowrey, K., Todorov, E. V., and Kakade, S. M · 2017
Cited alongside, same era.
Searching for Activation Functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
Cited alongside, same era.
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Model-ensemble trust-region policy optimization
Kurutach, T., Clavera, I., Duan, Y., Tamar, A., and Abbeel, P · 2018
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State Representation Learning for Control: An Overview
Lesort, T., Díaz-Rodríguez, N., Goudou, J.-F., and Filliat, D · 2018
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Decoupling Dynamics and Reward for Transfer Learning
Zhang, A., Satija, H., and Pineau, J · 2018
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Diagnosing bottlenecks in deep q-learning algorithms
Fu, J., Kumar, A., Soh, M., and Levine, S · 2019
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Agent modeling as auxiliary task for deep reinforcement learning
Hernandez-Leal, P., Kartal, B., and Taylor, M. E · 2019
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Sim-to-real transfer learning using robustified controllers in robotic tasks involving complex dynamics
v. Baar, J., Sullivan, A., Cordorel, R., Jha, D., Romeres, D., and Nikovski, D · 2019
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