Curiosity-Driven Exploration by Self-Supervised Prediction
暂无分享,去创建一个
Alexei A. Efros | Trevor Darrell | Deepak Pathak | Pulkit Agrawal | Trevor Darrell | Deepak Pathak | Pulkit Agrawal
[1] Stewart W. Wilson,et al. A Possibility for Implementing Curiosity and Boredom in Model-Building Neural Controllers , 1991 .
[2] Michael I. Jordan,et al. Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..
[3] S. Hochreiter,et al. REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NONDETERMINISTIC ENVIRONMENTS , 1995 .
[4] Michael I. Jordan,et al. An internal model for sensorimotor integration. , 1995, Science.
[5] Michael Kearns,et al. Efficient Reinforcement Learning in Factored MDPs , 1999, IJCAI.
[6] E. Deci,et al. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.
[7] Ronen I. Brafman,et al. R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..
[8] Nuttapong Chentanez,et al. Intrinsically Motivated Reinforcement Learning , 2004, NIPS.
[9] Chrystopher L. Nehaniv,et al. Empowerment: a universal agent-centric measure of control , 2005, 2005 IEEE Congress on Evolutionary Computation.
[10] Jesse Hoey,et al. An analytic solution to discrete Bayesian reinforcement learning , 2006, ICML.
[11] Pierre-Yves Oudeyer,et al. Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.
[12] Pierre-Yves Oudeyer,et al. What is Intrinsic Motivation? A Typology of Computational Approaches , 2007, Frontiers Neurorobotics.
[13] Jürgen Schmidhuber,et al. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.
[14] Yi Sun,et al. Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments , 2011, AGI.
[15] Doina Precup,et al. An information-theoretic approach to curiosity-driven reinforcement learning , 2012, Theory in Biosciences.
[16] P. Silvia. Curiosity and Motivation , 2012, The Oxford Handbook of Human Motivation.
[17] Pierre-Yves Oudeyer,et al. Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress , 2012, NIPS.
[18] Friedrich T. Sommer,et al. Learning and exploration in action-perception loops , 2013, Front. Neural Circuits.
[19] Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
[20] Andrew Schartmann. Super Mario Bros. , 2015 .
[21] Jonathan Tompson,et al. Unsupervised Feature Learning from Temporal Data , 2015, ICLR.
[22] Nitish Srivastava. Unsupervised Learning of Visual Representations using Videos , 2015 .
[23] Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
[24] Kristen Grauman,et al. Learning Image Representations Tied to Ego-Motion , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] Jitendra Malik,et al. Learning to See by Moving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[26] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[27] Honglak Lee,et al. Action-Conditional Video Prediction using Deep Networks in Atari Games , 2015, NIPS.
[28] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[29] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[30] Filip De Turck,et al. VIME: Variational Information Maximizing Exploration , 2016, NIPS.
[31] Benjamin Van Roy,et al. Deep Exploration via Bootstrapped DQN , 2016, NIPS.
[32] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[33] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[34] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Jitendra Malik,et al. Learning to Poke by Poking: Experiential Learning of Intuitive Physics , 2016, NIPS.
[36] Wojciech Jaskowski,et al. ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).
[37] J. Schulman,et al. Variational Information Maximizing Exploration , 2016 .
[38] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[39] Wojciech Zaremba,et al. OpenAI Gym , 2016, ArXiv.
[40] Filip De Turck,et al. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning , 2016, NIPS.
[41] Vladlen Koltun,et al. Learning to Act by Predicting the Future , 2016, ICLR.
[42] Justin Fu,et al. EX2: Exploration with Exemplar Models for Deep Reinforcement Learning , 2017, NIPS.
[43] Daan Wierstra,et al. Variational Intrinsic Control , 2016, ICLR.
[44] Trevor Darrell,et al. Loss is its own Reward: Self-Supervision for Reinforcement Learning , 2016, ICLR.
[45] Razvan Pascanu,et al. Learning to Navigate in Complex Environments , 2016, ICLR.
[46] Tom Schaul,et al. Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.
[47] Ilya Kostrikov,et al. Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play , 2017, ICLR.