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Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations.
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End-to-end training of deep visuomotor policies
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Unsupervised learning of disentangled representations from video
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Video pixel networks
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Video frame synthesis using deep voxel flow
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Why is posterior sampling better than optimism for reinforcement learning?
Ian Osband and Benjamin Van Roy · 2017
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Imagination-augmented agents for deep reinforcement learning
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2017
Hierarchical long-term video prediction without supervision
Nevan Wichers, Ruben Villegas, Dumitru Erhan, and Honglak Lee · 2018
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Motion perception in reinforcement learning with dynamic objects
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Robonet: Large-scale multi-robot learning
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Off-policy deep reinforcement learning without exploration
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Deepmdp: Learning continuous latent space models for representation learning
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Stochastic variational video prediction
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Stochastic video generation with a learned prior
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
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Learning to decompose and disentangle representations for video prediction
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Mastering atari, go, chess and shogi by planning with a learned model
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Clinical prediction models
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High fidelity video prediction with large stochastic recurrent neural networks
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V Le, and Honglak Lee · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Reinforcement learning in healthcare: a survey
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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