Self-Modeling Neural Systems
暂无分享,去创建一个
[1] Longxin Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.
[2] Robert M. Sapolsky,et al. Why zebras don't get ulcers : an updated guide to stress, stress-related diseases, and coping , 1994 .
[3] Sebastian Thrun,et al. A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge , 2006, AI Mag..
[4] Timothy P. Lillicrap,et al. Sensitivity Derivatives for Flexible Sensorimotor Learning , 2008, Neural Computation.
[5] Yuval Tassa. Fast Model Predictive Control for Reactive Robotic Swimming , 2010 .
[6] E. Gat. On Three-Layer Architectures , 1997 .
[7] G. Gaál. Relationship of calculating the Jacobian matrices of nonlinear systems and population coding algorithms in neurobiology , 1995 .
[8] Chun-Ta Chen,et al. A reflexive vehicle control architecture based on a neural model of the cockroach escape response , 2012, J. Syst. Control. Eng..
[9] Y. Sugita. Global plasticity in adult visual cortex following reversal of visual input , 1996, Nature.
[10] S. Haykin,et al. Adaptive Filter Theory , 1986 .
[11] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[12] F. Attneave. Some informational aspects of visual perception. , 1954, Psychological review.
[13] L. Mcbride,et al. Optimization of time-varying systems , 1965 .
[14] Francis Crick,et al. The recent excitement about neural networks , 1989, Nature.
[15] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[16] Stefan Schaal,et al. Reinforcement learning of motor skills in high dimensions: A path integral approach , 2010, 2010 IEEE International Conference on Robotics and Automation.
[17] Michael I. Jordan,et al. Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..
[18] Geoffrey E. Hinton,et al. Learning Representations by Recirculation , 1987, NIPS.
[19] R. Bellman. Dynamic programming. , 1957, Science.
[20] Geoffrey E. Hinton,et al. Training Recurrent Neural Networks , 2013 .
[21] D. Wolpert,et al. Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.
[22] Christopher G. Atkeson,et al. Efficient robust policy optimization , 2012, 2012 American Control Conference (ACC).
[23] David Sussillo,et al. Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks , 2013, Neural Computation.
[24] Peter Dayan,et al. Goal-directed control and its antipodes , 2009, Neural Networks.
[25] Emanuel Todorov,et al. Revision of JN-RM-3106-07 Recurrent neural networks trained in the presence of noise give rise to mixed muscle-movement representations , 2008 .
[26] Michael Mandelstam,et al. On the Bandwagon? , 2007 .
[27] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[28] R. J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[29] D. Wolpert,et al. Principles of sensorimotor learning , 2011, Nature Reviews Neuroscience.
[30] Rodney A. Brooks,et al. A Robust Layered Control Syste For A Mobile Robot , 2022 .
[31] Yann LeCun,et al. A theoretical framework for back-propagation , 1988 .
[32] M. Kawato,et al. A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.
[33] David J. Fleet,et al. Optimizing walking controllers , 2009, ACM Trans. Graph..
[34] Donald A. Sofge,et al. Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches , 1992 .
[35] Zoran Popovic,et al. Discovery of complex behaviors through contact-invariant optimization , 2012, ACM Trans. Graph..
[36] A. Newell. Unified Theories of Cognition , 1990 .
[37] T. Jessell,et al. Clarke's Column Neurons as the Focus of a Corticospinal Corollary Circuit , 2010, Nature Neuroscience.
[38] Bernard Widrow,et al. Punish/Reward: Learning with a Critic in Adaptive Threshold Systems , 1973, IEEE Trans. Syst. Man Cybern..
[39] E. Todorov. Optimality principles in sensorimotor control , 2004, Nature Neuroscience.
[40] John E. Hershey,et al. Computation , 1991, Digit. Signal Process..
[41] Razvan V. Florian,et al. Correct equations for the dynamics of the cart-pole system , 2005 .
[42] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[43] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[44] J. Krakauer,et al. Error correction, sensory prediction, and adaptation in motor control. , 2010, Annual review of neuroscience.
[45] J. Krakauer,et al. An Implicit Plan Overrides an Explicit Strategy during Visuomotor Adaptation , 2006, The Journal of Neuroscience.
[46] Sebastian Thrun,et al. Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.
[47] Norbert Wiener,et al. Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine , 1965 .
[48] Emanuel Todorov,et al. Real-time motor control using recurrent neural networks , 2009, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning.
[49] G. Stratton. Some preliminary experiments on vision without inversion of the retinal image. , 1896 .
[50] Pawel Wawrzynski,et al. Real-time reinforcement learning by sequential Actor-Critics and experience replay , 2009, Neural Networks.
[51] Kenji Doya,et al. Reinforcement Learning in Continuous Time and Space , 2000, Neural Computation.
[52] Gerhardt von Bonin,et al. Cybernetics or control and communication in the animal and the machine: Norbert wiener, 1948. 194 pp. New York: John Wiley & Sons, Inc. Paris: Hermann et cie , 1949 .
[53] Michael I. Jordan,et al. Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.
[54] Andrew Y. Ng,et al. Policy search via the signed derivative , 2009, Robotics: Science and Systems.
[55] Russ Tedrake,et al. LQR-trees: Feedback motion planning on sparse randomized trees , 2009, Robotics: Science and Systems.
[56] B. Widrow,et al. The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.
[57] Shalabh Bhatnagar,et al. Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation , 2009, NIPS.
[58] Robert F. Stengel,et al. Optimal Control and Estimation , 1994 .
[59] T. Flash,et al. The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[60] Marc'Aurelio Ranzato,et al. Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.
[61] C. Brinkman,et al. Plasticity of motor behavior in monkeys with crossed forelimb nerves. , 1983, Science.
[62] K. Lashley. Basic neural mechanisms in behavior. , 1930 .