Deep Learning with R Cookbook

How to do it...

In the Getting ready section, we defined our RL problem. We know that to solve a model-based RL problem, we need a transition probability matrix and a reward matrix:

1. Let's start by defining the transition probability matrices. We start off by defining the probabilities for all the actions at each state. The sum of the probabilities in each row sums up to 1:

# Up up=matrix(c( 0.9, 0.1, 0, 0, 0.2, 0.7, 0.1, 0, 0, 0, 0.1, 0.9, 0, 0, 0, 1), nrow=4,ncol=4,byrow=TRUE)# Down down=matrix(c(0.1, 0, 0, 0.9, 0, 0.8, 0.2, 0, 0, 0.2, 0.8, 0, 0, 0, 0.8, 0.2), nrow=4,ncol=4,byrow=TRUE)# Left left=matrix(c(1, 0, 0, 0, 0.9, 0.1, 0, 0, 0, 0.8, 0.2, 0, 0, 0, 0, 1), nrow=4,ncol=4,byrow=TRUE)# Right right=matrix(c(0.1, 0.9, 0, 0, 0.1, 0.2, 0.7, ...