Learning to Explore via Meta-Policy Gradient
暂无分享,去创建一个
Qiang Liu | Jian Peng | Liang Zhao | Tianbing Xu | Qiang Liu | Jian Peng | Liang Zhao | Tianbing Xu
[1] Jürgen Schmidhuber,et al. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.
[2] Andrew Y. Ng,et al. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.
[3] Chris Watkins,et al. Learning from delayed rewards , 1989 .
[4] Sergey Levine,et al. End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..
[5] Ryan P. Adams,et al. Gradient-based Hyperparameter Optimization through Reversible Learning , 2015, ICML.
[6] Pierre-Yves Oudeyer,et al. What is Intrinsic Motivation? A Typology of Computational Approaches , 2007, Frontiers Neurorobotics.
[7] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .
[8] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[9] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[10] Manuel Lopes,et al. Learning exploration strategies in model-based reinforcement learning , 2013, AAMAS.
[11] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[12] Pieter Abbeel,et al. Evolved Policy Gradients , 2018, NeurIPS.
[13] Longxin Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.
[14] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[15] Benjamin Van Roy,et al. Deep Exploration via Bootstrapped DQN , 2016, NIPS.
[16] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[17] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[18] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[19] Peter L. Bartlett,et al. RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning , 2016, ArXiv.
[20] Filip De Turck,et al. VIME: Variational Information Maximizing Exploration , 2016, NIPS.
[21] Misha Denil,et al. Learning to Learn without Gradient Descent by Gradient Descent , 2016, ICML.
[22] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[23] Pawel Wawrzynski,et al. Real-time reinforcement learning by sequential Actor-Critics and experience replay , 2009, Neural Networks.
[24] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[25] Filip De Turck,et al. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning , 2016, NIPS.
[26] G. Uhlenbeck,et al. On the Theory of the Brownian Motion , 1930 .
[27] Marcin Andrychowicz,et al. Parameter Space Noise for Exploration , 2017, ICLR.
[28] 一樹 美添,et al. 5分で分かる! ? 有名論文ナナメ読み:Silver, D. et al. : Mastering the Game of Go without Human Knowledge , 2018 .
[29] Zeb Kurth-Nelson,et al. Learning to reinforcement learn , 2016, CogSci.
[30] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[31] Peter Stone,et al. Intrinsically motivated model learning for developing curious robots , 2017, Artif. Intell..
[32] Benjamin Van Roy,et al. Generalization and Exploration via Randomized Value Functions , 2014, ICML.
[33] Yuval Tassa,et al. Emergence of Locomotion Behaviours in Rich Environments , 2017, ArXiv.
[34] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[35] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
[38] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.