暂无分享,去创建一个
Martin A. Riedmiller | Thomas Lampe | Abbas Abdolmaleki | Jost Tobias Springenberg | Nicolas Heess | Martin Riedmiller | Markus Wulfmeier | Roland Hafner | Michael Neunert | Tim Hertweck | Noah Siegel | N. Heess | Roland Hafner | T. Lampe | Markus Wulfmeier | A. Abdolmaleki | Michael Neunert | Tim Hertweck | Noah Siegel | J. T. Springenberg
[1] John R. Anderson,et al. The Transfer of Cognitive Skill , 1989 .
[2] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[3] S. Srihari. Mixture Density Networks , 1994 .
[4] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[5] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[6] R. French. Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.
[7] Doina Precup,et al. Temporal abstraction in reinforcement learning , 2000, ICML 2000.
[8] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[9] Thomas G. Dietterich,et al. To transfer or not to transfer , 2005, NIPS 2005.
[10] Emilio Soria Olivas,et al. Handbook of Research on Machine Learning Applications and Trends : Algorithms , Methods , and Techniques , 2009 .
[11] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[12] Jan Peters,et al. Hierarchical Relative Entropy Policy Search , 2014, AISTATS.
[13] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[14] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[15] Yuval Tassa,et al. Learning Continuous Control Policies by Stochastic Value Gradients , 2015, NIPS.
[16] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[17] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[18] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[19] Marc G. Bellemare,et al. Safe and Efficient Off-Policy Reinforcement Learning , 2016, NIPS.
[20] Yuval Tassa,et al. Learning and Transfer of Modulated Locomotor Controllers , 2016, ArXiv.
[21] Tom Schaul,et al. FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.
[22] Doina Precup,et al. The Option-Critic Architecture , 2016, AAAI.
[23] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[24] Dan Klein,et al. Modular Multitask Reinforcement Learning with Policy Sketches , 2016, ICML.
[25] Marcin Andrychowicz,et al. Hindsight Experience Replay , 2017, NIPS.
[26] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[27] Pieter Abbeel,et al. Mutual Alignment Transfer Learning , 2017, CoRL.
[28] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[29] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[30] Yee Whye Teh,et al. Distral: Robust multitask reinforcement learning , 2017, NIPS.
[31] Tom Schaul,et al. Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.
[32] Sergey Levine,et al. Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.
[33] Karol Hausman,et al. Learning an Embedding Space for Transferable Robot Skills , 2018, ICLR.
[34] Yee Whye Teh,et al. Transferring Task Goals via Hierarchical Reinforcement Learning , 2018 .
[35] Yuval Tassa,et al. Relative Entropy Regularized Policy Iteration , 2018, ArXiv.
[36] Shimon Whiteson,et al. TACO: Learning Task Decomposition via Temporal Alignment for Control , 2018, ICML.
[37] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[38] Joelle Pineau,et al. An Inference-Based Policy Gradient Method for Learning Options , 2018, ICML.
[39] Doina Precup,et al. When Waiting is not an Option : Learning Options with a Deliberation Cost , 2017, AAAI.
[40] Yuval Tassa,et al. Maximum a Posteriori Policy Optimisation , 2018, ICLR.
[41] Yuval Tassa,et al. DeepMind Control Suite , 2018, ArXiv.
[42] Martin A. Riedmiller,et al. Learning by Playing - Solving Sparse Reward Tasks from Scratch , 2018, ICML.
[43] Sergey Levine,et al. Latent Space Policies for Hierarchical Reinforcement Learning , 2018, ICML.
[44] Doina Precup,et al. The Termination Critic , 2019, AISTATS.
[45] Yee Whye Teh,et al. Information asymmetry in KL-regularized RL , 2019, ICLR.
[46] Jaime G. Carbonell,et al. Characterizing and Avoiding Negative Transfer , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Shimon Whiteson,et al. DAC: The Double Actor-Critic Architecture for Learning Options , 2019, NeurIPS.
[48] Yee Whye Teh,et al. Exploiting Hierarchy for Learning and Transfer in KL-regularized RL , 2019, ArXiv.
[49] Sergio Gomez Colmenarejo,et al. TF-Replicator: Distributed Machine Learning for Researchers , 2019, ArXiv.
[50] Sergey Levine,et al. Near-Optimal Representation Learning for Hierarchical Reinforcement Learning , 2018, ICLR.
[51] Shimon Whiteson,et al. Multitask Soft Option Learning , 2019, UAI.
[52] Jakub W. Pachocki,et al. Learning dexterous in-hand manipulation , 2018, Int. J. Robotics Res..