Batch Mode TD($\lambda$ ) for Controlling Partially Observable Gene Regulatory Networks
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[1] Hidde de Jong,et al. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..
[2] Michael I. Jordan,et al. Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems , 1994, NIPS.
[3] E. Dougherty,et al. Gene perturbation and intervention in probabilistic Boolean networks. , 2002, Bioinformatics.
[4] Andrew McCallum,et al. Instance-Based State Identification for Reinforcement Learning , 1994, NIPS.
[5] Chris Watkins,et al. Learning from delayed rewards , 1989 .
[6] Michael P. Verdicchio,et al. Template-based intervention in Boolean network models of biological systems , 2014, EURASIP J. Bioinform. Syst. Biol..
[7] Nikos A. Vlassis,et al. Perseus: Randomized Point-based Value Iteration for POMDPs , 2005, J. Artif. Intell. Res..
[8] A. Datta,et al. On approximate stochastic control in genetic regulatory networks. , 2007, IET systems biology.
[9] Aurélien Naldi,et al. Dynamically consistent reduction of logical regulatory graphs , 2011, Theor. Comput. Sci..
[10] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[11] Reda Alhajj,et al. The Benefit of Decomposing POMDP for Control of Gene Regulatory Networks , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.
[12] Edward R. Dougherty,et al. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..
[13] Peter Vrancx,et al. Reinforcement Learning: State-of-the-Art , 2012 .
[14] Longxin Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching , 2004, Machine Learning.
[15] Michael P. Verdicchio,et al. Identifying Targets for intervention by Analyzing Basins of Attraction , 2011, Pacific Symposium on Biocomputing.
[16] Reda Alhajj,et al. Employing Batch Reinforcement Learning to Control Gene Regulation Without Explicitly Constructing Gene Regulatory Networks , 2013, IJCAI.
[17] Pierre Geurts,et al. Tree-Based Batch Mode Reinforcement Learning , 2005, J. Mach. Learn. Res..
[18] Dirk Ormoneit,et al. Kernel-Based Reinforcement Learning , 2017, Encyclopedia of Machine Learning and Data Mining.
[19] Daniel Bryce,et al. Planning interventions in biological networks , 2010, TIST.
[20] Edward R. Dougherty,et al. CAN MARKOV CHAIN MODELS MIMIC BIOLOGICAL REGULATION , 2002 .
[21] Aniruddha Datta,et al. Optimal infinite horizon control for probabilistic Boolean networks , 2006, 2006 American Control Conference.
[22] Michael Ruogu Zhang,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.
[23] Aniruddha Datta,et al. On Reinforcement Learning in Genetic Regulatory Networks , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.
[24] Leslie Pack Kaelbling,et al. Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..
[25] Marco Wiering,et al. Reinforcement Learning , 2014, Adaptation, Learning, and Optimization.
[26] E. Dougherty,et al. CONTROL OF STATIONARY BEHAVIOR IN PROBABILISTIC BOOLEAN NETWORKS BY MEANS OF STRUCTURAL INTERVENTION , 2002 .
[27] Mauricio Barahona,et al. Toggling a Genetic Switch Using Reinforcement Learning , 2013, ArXiv.
[28] Andrew McCallum,et al. Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State , 1995, ICML.
[29] Michael I. Jordan,et al. Learning Without State-Estimation in Partially Observable Markovian Decision Processes , 1994, ICML.
[30] Aniruddha Datta,et al. External control in Markovian genetic regulatory networks: the imperfect information case , 2004, Bioinform..
[31] Andrew McCallum,et al. Overcoming Incomplete Perception with Utile Distinction Memory , 1993, ICML.
[32] Xin Liu,et al. Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia , 2011, PLoS Comput. Biol..
[33] Alexander J. Hartemink,et al. Informative Structure Priors: Joint Learning of Dynamic Regulatory Networks from Multiple Types of Data , 2004, Pacific Symposium on Biocomputing.
[34] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[35] N. Sampas,et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling , 2000, Nature.
[36] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[37] A. Datta,et al. External Control in Markovian Genetic Regulatory Networks , 2003, Proceedings of the 2003 American Control Conference, 2003..
[38] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[39] Bart De Schutter,et al. Reinforcement Learning and Dynamic Programming Using Function Approximators , 2010 .