Accumulator Intrinsic Motivation Block φ L 1 φ L k Input Observations ( x ) Reward Switch
暂无分享,去创建一个
[1] Jürgen Schmidhuber,et al. Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.
[2] Michael Werman,et al. An On-Line Agglomerative Clustering Method for Nonstationary Data , 1999, Neural Computation.
[3] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[4] Michail G. Lagoudakis,et al. Model-Free Least-Squares Policy Iteration , 2001, NIPS.
[5] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[6] R. Coifman,et al. Diffusion Wavelets , 2004 .
[7] Daoqiang Zhang,et al. Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures , 2005, Neural Processing Letters.
[8] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[9] Sridhar Mahadevan,et al. Proto-value functions: developmental reinforcement learning , 2005, ICML.
[10] A. Barto,et al. Intrinsic Motivation For Reinforcement Learning Systems , 2005 .
[11] Giulio Sandini,et al. The iCub humanoid robot: an open platform for research in embodied cognition , 2008, PerMIS.
[12] Andrew G. Barto,et al. Efficient skill learning using abstraction selection , 2009, IJCAI 2009.
[13] Andrew G. Barto,et al. Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining , 2009, NIPS.
[14] Scott Kuindersma,et al. Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories , 2010, NIPS.
[15] Jürgen Schmidhuber,et al. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) , 2010, IEEE Transactions on Autonomous Mental Development.
[16] Robert A. Legenstein,et al. Reinforcement Learning on Slow Features of High-Dimensional Input Streams , 2010, PLoS Comput. Biol..
[17] Jürgen Schmidhuber,et al. AutoIncSFA and vision-based developmental learning for humanoid robots , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.
[18] Henning Sprekeler,et al. On the Relation of Slow Feature Analysis and Laplacian Eigenmaps , 2011, Neural Computation.
[19] Jürgen Schmidhuber,et al. Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams , 2012, Neural Computation.