Functional approximations and dynamic programming

Functional Approximations and Dynamic Programming

Functional Programming with Dynamic Binding

Dynamic binding is a runtime operation which looks up some names in some environments: examples are quote and eval in LISP or object-oriented message passing. We present a functional language, in the spirit of ML or Haskell, which supports dynamic binding. At the surface level, the language supports constructs such as extensible records, variants, extensible case selection, quoted terms, and ca...

Dynamic Programming Approximations for Partially Observable Stochastic Games

Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes with a price, namely a high computational cost. Solving POSGs optimally quickly becomes intractable after a few decision cycles. Our main contribution is to provide bounded approximation techniques, which enable us to sca...

Dynamic Programming Approximations for a Stochastic Inventory Routing Problem

This work is motivated by the need to solve the inventory routing problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers’ inventories, and decide when and how much inventory should be replenished at each customer. The inventory routing problem attempts to coordinate inventory replenishment and transportation i...

Dynamic Programming Algorithm for Training Functional Networks

The paper proposes a dynamic programming algorithm for training of functional networks. The algorithm considers each node as a state. The problem is formulated as finding the sequence of states which minimizes the sum of the squared errors approximation. Each node is optimized with regard to its corresponding neural functions and its estimated neuron functions. The dynamic programming algorithm...