暂无分享,去创建一个
[1] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Pieter Abbeel,et al. Domain Randomization for Active Pose Estimation , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[4] Samy Bengio,et al. A Study on Overfitting in Deep Reinforcement Learning , 2018, ArXiv.
[5] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[6] Silvio Savarese,et al. SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark , 2018, CoRL.
[7] Marlos C. Machado,et al. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents , 2017, J. Artif. Intell. Res..
[8] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[9] Wojciech Zaremba,et al. Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[10] Junmo Kim,et al. Learning Not to Learn: Training Deep Neural Networks With Biased Data , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Taehoon Kim,et al. Quantifying Generalization in Reinforcement Learning , 2018, ICML.
[12] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[13] Sergey Levine,et al. High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.
[14] Ming-Yu Liu,et al. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents , 2017, IJCAI.
[15] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Amos J. Storkey,et al. Exploration by Random Network Distillation , 2018, ICLR.
[17] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[18] Razvan Pascanu,et al. Policy Distillation , 2015, ICLR.
[19] Marcin Andrychowicz,et al. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[20] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[21] Marlos C. Machado,et al. Generalization and Regularization in DQN , 2018, ArXiv.
[22] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[23] Abhinav Gupta,et al. Environment Probing Interaction Policies , 2019, ICLR.
[24] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Graham W. Taylor,et al. Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.
[26] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[27] Nando de Freitas,et al. Playing hard exploration games by watching YouTube , 2018, NeurIPS.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[30] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[31] Dawn Xiaodong Song,et al. Assessing Generalization in Deep Reinforcement Learning , 2018, ArXiv.
[32] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[33] Jing Peng,et al. Function Optimization using Connectionist Reinforcement Learning Algorithms , 1991 .
[34] Sandy H. Huang,et al. Adversarial Attacks on Neural Network Policies , 2017, ICLR.
[35] Zeb Kurth-Nelson,et al. Learning to reinforcement learn , 2016, CogSci.
[36] Quoc V. Le,et al. AutoAugment: Learning Augmentation Policies from Data , 2018, ArXiv.
[37] Yoav Goldberg,et al. Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation , 2018, ICML.
[38] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[39] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[40] Quoc V. Le,et al. Unsupervised Data Augmentation , 2019, ArXiv.
[41] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[42] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[43] Youyong Kong,et al. Deep Direct Reinforcement Learning for Financial Signal Representation and Trading , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[44] Erica Moodie,et al. Dynamic treatment regimes. , 2004, Clinical trials.
[45] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[46] Joelle Pineau,et al. A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning , 2018, ArXiv.
[47] Tom Schaul,et al. StarCraft II: A New Challenge for Reinforcement Learning , 2017, ArXiv.
[48] Shane Legg,et al. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , 2018, ICML.
[49] Christopher Burgess,et al. DARLA: Improving Zero-Shot Transfer in Reinforcement Learning , 2017, ICML.
[50] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[51] Sergey Levine,et al. Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL , 2018, ICLR.
[52] Albin Cassirer,et al. Randomized Prior Functions for Deep Reinforcement Learning , 2018, NeurIPS.