The following derivations will use characters defined as in the required clause.
Q-learning methodology is based on the Bellman equation .
where,
E: pending
t + 1 : next state
: discount factor
To rephrase the above equation as a Q-value: —
Optimal Q value is defined as
Policy iteration: is the process of determining the optimal policy for the model, which consists of the following two steps:
Steps included are —
= any valid random number
= any A (s) randomly selected
while ( ) { for each s in S {}}
while (true) for each s in S { if ( ) if ( ) break from both loo ps } return V,
Working steps:
while ( return) { for each s in S { }}