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Title: Zap Q-Learning - A User's Guide
The authors develop a theory characterizing optimal stopping times for discrete-time ergodic Markov processes with discounted rewards. The theory differs from prior work by its view of per-stage and terminal reward functions as elements of a certain Hilbert space. In addition to a streamlined analysis establishing existence and uniqueness of a solution to Bellman's equation, this approach provides an elegant framework for the study of approximate solutions. In particular, the authors propose a stochastic approximation algorithm that tunes weights of a linear combination of basis functions in order to approximate a value function. They prove that this algorithm converges (almost surely) and that the limit of convergence has some desirable properties. The utility of the approximation method is illustrated via a computational case study involving the pricing of a path dependent financial derivative security that gives rise to an optimal stopping problem with a 100-dimensional state space  more » « less
Award ID(s):
1646229
PAR ID:
10211835
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proc. of the Fifth Indian Control Conference
Page Range / eLocation ID:
10 to 15
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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