Approximate Policy Iteration for Markov Decision Processes via Quantitative Adaptive Aggregations ? ˇ ska1,2 , and Marta Kwiatkowska1 Alessandro Abate1 , Milan Ceˇ 2

First Page		Document Content
Date: 2016-07-08 04:40:26 Dynamic programming Equations Stochastic control Systems theory Control theory Systems science Markov processes Mathematics Markov decision process Mathematical optimization Bellman equation Reinforcement learning		Approximate Policy Iteration for Markov Decision Processes via Quantitative Adaptive Aggregations ? ˇ ska1,2 , and Marta Kwiatkowska1 Alessandro Abate1 , Milan Ceˇ 2 Add to Reading List Source URL: qav.comlab.ox.ac.uk Download Document from Source Website File Size: 190,84 KB Share Document on Facebook

	Model Predictive Control Anders Hansson October 7, 2010 It is well-known that it is complicated to derive optimal control in feedback form. Usually one has to solve the dynamic programming or Bellman equation, DocID: 1uLN4 - View Document
	DOC Document DocID: 1rtSh - View Document
	Recursive utility using the stochastic maximum principle Knut K. Aase ∗ DocID: 1rbM8 - View Document
	History dependent public policies David Evans ∗ Thomas J. Sargent‡† DocID: 1r49V - View Document
	American Economic Review 2012, 102(4): 1663–1691 http://dx.doi.orgaerRisk Aversion and the Labor Margin in Dynamic Equilibrium Models† By Eric T. Swanson* DocID: 1qPn8 - View Document