Error Propagation For Approximate Policy And Value Iteration
from GoogleSign inHidden fieldsBooksbooks.google.comhttps://books.google.com/books/about/Regularized_Approximate_Policy_Iteration.html?id=kEeXCAAAQBAJ&utm_source=gb-gplus-shareRegularized Approximate Policy Iteration using kernel for on-line Reinforcement LearningMy libraryHelpAdvanced Book SearchGet print bookNo eBook availablegennaro espositoAll sellers»Get Textbooks on Google PlayRent and save from the world's largest eBookstore. Read, highlight, and take notes, across web, tablet, and phone.Go to Google Play Now »Regularized Approximate Policy Iteration using kernel for on-line Reinforcement LearningGennaro Esposito, PhDgennaro esposito, Jun 30, 2015 - 196 pages 0 Reviewshttps://books.google.com/books/about/Regularized_Approximate_Policy_Iteration.html?id=kEeXCAAAQBAJ Preview this book » What people are saying-Write a reviewWe haven't found any reviews in the usual places.Selected pagesTitle PageTable of ContentsIndexContentsReinforcement Learning1 Generalization Problem in RL19 Kernel Based Approximate Policy Iteration39 APIBRM Theoretical Analysis71 APIBRM Experimental Analysis89 Conclusions and Future Work117 Acknowledgments121 Bibliography123 Appendices133 Statistical Learning135 Kernel Methods143 Support Vector Machines151 Common terms and phrasesaction value function agent assume basis functions batch behavior policy Bellman equation Bellman operator bicycle bound BRMe cart pole cMDP compute consider control problem convergence covering number defined distribution dynamics empirical risk episodes estimate feature space finite fixed point function approximation function f function space given Hence Hilbert space hypothesis space initial state S0 inner product Inverted pendulum kernel function KKT conditions learning algorithm Lemma linear loss function LSPI Machine Learning mapping matrix Method-3 methods minimizing non-parametric offline optimal policy optimization problem parameters performance policy evaluation policy improvement Policy Iteration probability Q-learning quadratic regression regularized Reinforcement Learning Rep(f result reward function RKHS RL algorithms SARSA sequence simulation solution solve space H stationary stationary policy steps stochastic Support Vector Machines support vectors Theorem training set transition uniform convergence update value function approximationBiblio
from GoogleSign inHidden fieldsBooksbooks.google.com - In April 2007, the Deutsche Forschungsgemeinschaft (DFG) approved the Priority Program 1324 “Mathematical Methods for Extracting Quantifiable Information from Complex Systems.” This volume presents a comprehensive overview of the most important results obtained over the course of the program. Mathematical...https://books.google.com/books/about/Extraction_of_Quantifiable_Information_f.html?id=YilgBQAAQBAJ&utm_source=gb-gplus-shareExtraction of Quantifiable Information from Complex SystemsMy libraryHelpAdvanced Book SearchEBOOK FROM $39.28Get this book in printSpringer ShopAmazon.comBarnes&Noble.comBooks-A-MillionIndieBoundFind in a libraryAll sellers»Extraction of Quantifiable Information from Complex SystemsStephan Dahlke, Wolfgang Dahmen, Michael Griebel, Wolfgang Hackbusch, Klaus Ritter, Reinhold Schneider, Christoph Schwab, Harry YserentantSpringer, Nov 13, 2014 - Mathematics https://books.google.com/books?id=kEeXCAAAQBAJ&pg=PA127&lpg=PA127&dq=error+propagation+for+approximate+policy+and+value+iteration&source=bl&ots=i12kcDmDIt&sig=VgG79lZqjVw6BUxCvpcwKT4hfFU&hl=en&sa=X&ved=0ahUKEwjxk_aFt9LP - 432 pages 0 Reviewshttps://books.google.com/books/about/Extraction_of_Quantifiable_Information_f.html?id=YilgBQAAQBAJIn April 2007, the Deutsche Forschungsgemeinschaft (DFG) approved the Priority Program 1324 “Mathematical Methods for Extracting Quantifiable Information from Complex Systems.” This volume presents a comprehensive overview of the most important results obtained over the course of the program. Mathematical models of complex systems provide the foundation for further technological developments https://books.google.com/books?id=YilgBQAAQBAJ&pg=PA193&lpg=PA193&dq=error+propagation+for+approximate+policy+and+value+iteration&source=bl&ots=_3MJxY0YQf&sig=PVRPKw7xRrwfpR3QrgMN9ifg6O8&hl=en&sa=X&ved=0ahUKEwjxk_aFt9LP in science, engineering and computational finance. Motivated by the trend toward steadily increasing computer power, ever more realistic models have been developed in recent years. These models have also become increasingly complex, and their numerical treatment poses serious challenges. Recent developments in mathematics suggest that, in the long run, much more powerful numerical solution strategies could be derived if the interconnections between the different fields of research were systematically exploited at a conceptual level. Accordingly, a deeper understanding of the mathematical foundations as well as the development of new and efficient numerical algorithms were among the main goals of this Priority Program. The treatment of high-dimensional systems is clearly one of the most challenging tasks in applied mathematics today. Since the problem of high-dimensionality appears in many fields of application, the above-mentioned synergy and cross-fertilization effects were expected to make a great impact. To be truly successful, the following issues had to be kept in mind: theoretical research and practical applications had to be develope
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