Policy Learning in Static Bayesian Networks

This function is only available in Validation Mode for static networks that have both Decision Nodes and Utility Nodes.

In this context, BayesiaLab can run an algorithm — based on dynamic programming — to find the optimal policy among all the possible decision states specified in the Decision Nodes.

The obtained policy describes the decisions to take in order to obtain the maximum expected utility.

The global utility is defined by the sum of all the utilities of the network. The resulting policy can be viewed directly read in the Quality Table of each Decision Node. The quality corresponds to the expected utility if one applies the action and then applies the actions specified by BayesiaLab.

The action to apply, the one with the greatest quality, appears in light blue in the quality table and in the monitors.