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Sergey Levine | Dumitru Erhan | Roy H. Campbell | Lukasz Kaiser | Chelsea Finn | George Tucker | Mohammad Babaeizadeh | Henryk Michalewski | Piotr Milos | Konrad Czechowski | Ryan Sepassi | Blazej Osinski | Piotr Kozakowski | S. Levine | D. Erhan | Lukasz Kaiser | G. Tucker | R. Campbell | Chelsea Finn | Ryan Sepassi | M. Babaeizadeh | Piotr Milos | B. Osinski | K. Czechowski | Piotr Kozakowski | Henryk Michalewski | H. Michalewski | Afroz Mohiuddin
[1] Richard S. Sutton,et al. Dyna, an integrated architecture for learning, planning, and reacting , 1990, SGAR.
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] Thomas G. Dietterich. Adaptive computation and machine learning , 1998 .
[4] Jürgen Schmidhuber,et al. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.
[5] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[6] Jan Peters,et al. A Survey on Policy Search for Robotics , 2013, Found. Trends Robotics.
[7] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[8] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[9] Yuval Tassa,et al. Learning Continuous Control Policies by Stochastic Value Gradients , 2015, NIPS.
[10] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[11] Samy Bengio,et al. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks , 2015, NIPS.
[12] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[13] Honglak Lee,et al. Action-Conditional Video Prediction using Deep Networks in Atari Games , 2015, NIPS.
[14] Sanem Sariel,et al. Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning , 2015, IEEE Transactions on Computational Intelligence and AI in Games.
[15] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.
[16] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[17] Martial Hebert,et al. Improved Learning of Dynamics Models for Control , 2016, ISER.
[18] Katja Hofmann,et al. A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games , 2016, ICLR 2016.
[19] Sergey Levine,et al. Deep spatial autoencoders for visuomotor learning , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[20] Elman Mansimov,et al. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation , 2017, NIPS.
[21] Stephen Tyree,et al. Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU , 2016, ICLR.
[22] Joshua B. Tenenbaum,et al. Human Learning in Atari , 2017, AAAI Spring Symposia.
[23] Gabriel Kalweit,et al. Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning , 2017, CoRL.
[24] Sergey Levine,et al. Deep visual foresight for planning robot motion , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[25] Daan Wierstra,et al. Recurrent Environment Simulators , 2017, ICLR.
[26] Satinder Singh,et al. Value Prediction Network , 2017, NIPS.
[27] Sergey Levine,et al. Self-Supervised Visual Planning with Temporal Skip Connections , 2017, CoRL.
[28] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[29] Oriol Vinyals,et al. Neural Discrete Representation Learning , 2017, NIPS.
[30] Boyang Li,et al. Game Engine Learning from Video , 2017, IJCAI.
[31] Tom Schaul,et al. Rainbow: Combining Improvements in Deep Reinforcement Learning , 2017, AAAI.
[32] Peter Vrancx,et al. Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks , 2018 .
[33] Samy Bengio,et al. Discrete Autoencoders for Sequence Models , 2018, ArXiv.
[34] Pieter Abbeel,et al. Model-Ensemble Trust-Region Policy Optimization , 2018, ICLR.
[35] Marlos C. Machado,et al. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents , 2017, J. Artif. Intell. Res..
[36] Sergey Levine,et al. Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control , 2018, ArXiv.
[37] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[38] Sergey Levine,et al. Stochastic Variational Video Prediction , 2017, ICLR.
[39] Kostas Daniilidis,et al. Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction , 2018, ArXiv.
[40] Erik Talvitie,et al. The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces , 2018, ArXiv.
[41] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[42] Stephan Alaniz,et al. Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft , 2018, ArXiv.
[43] Marc G. Bellemare,et al. Dopamine: A Research Framework for Deep Reinforcement Learning , 2018, ArXiv.
[44] Haitham Bou-Ammar,et al. Model-Based Stabilisation of Deep Reinforcement Learning , 2018, ArXiv.
[45] Rémi Munos,et al. Observe and Look Further: Achieving Consistent Performance on Atari , 2018, ArXiv.
[46] Sergey Levine,et al. Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning , 2018, ArXiv.
[47] Jürgen Schmidhuber,et al. Recurrent World Models Facilitate Policy Evolution , 2018, NeurIPS.
[48] Kamyar Azizzadenesheli,et al. Sample-Efficient Deep RL with Generative Adversarial Tree Search , 2018, ArXiv.
[49] Jürgen Schmidhuber,et al. World Models , 2018, ArXiv.
[50] Matteo Hessel,et al. When to use parametric models in reinforcement learning? , 2019, NeurIPS.
[51] Pieter Abbeel,et al. Benchmarking Model-Based Reinforcement Learning , 2019, ArXiv.
[52] Sergey Levine,et al. Learning Powerful Policies by Using Consistent Dynamics Model , 2019, ArXiv.
[53] Nicolas Heess,et al. Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search , 2018, ICLR.
[54] Kacper Kielak. Do recent advancements in model-based deep reinforcement learning really improve data efficiency? , 2019 .
[55] Michael S. Ryoo,et al. Learning Real-World Robot Policies by Dreaming , 2018, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[56] Gregory D. Hager,et al. Visual Robot Task Planning , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[57] Ruben Villegas,et al. Learning Latent Dynamics for Planning from Pixels , 2018, ICML.