Abstract
In this paper, a forward method is introduced for solving the dynamic programming equations of Bellman. This is in contrast to most existing methods for dynamic programming which solve the problem backwards. A key advantage is that the forward dynamic programming approach can be systematically simplified to provide computation/optimality trade-offs. Such trade-offs are lacking in backwards iterative methods which tend to be “all or nothing” propositions. A second advantage is that the computation is independent of the state dimension. These properties together offer some promise for circumventing the “curse of dimensionality” on many problems of practical interest. Due to a strong connection with the work of Bellman on policy iteration, the method is denoted as the Iteration in Policy Space (IPS) algorithm. Several examples are given to demonstrate the general usefulness of the method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
