$(4$ scrreenshots phyton codes please use them $)(30$ points$)$ Implement a general policy iteration algorithm in Python to determine

the optimal policy for an MDP problem. For this, write three functions: $(1)$ Policy

evaluation that takes the MDP and a policy as an input and returns the state values,

$(2)$ policy improvement that takes the MDP$,$ a policy and the state values as an input

and returns an improved policy, and $(3)$ general policy iteration that calls the functions

$(1)$ and $(2)$ iteratively until the convergence criterion is met. In the Python template

$$Scheduling MDP DP HW$4.$py$$ you will find the core structure of these three

functions with missing code sections marked as #CODE HERE.

$($b$)(10$ points$)$ Solve the biopharmaceutical batch fermentation problem from Question $3$

in Python using policy or value iteration. The Python template $$Scheduling MDP Biopharma Case provides you the parameters and some pre$-$filled code sections for this case. You have

to define the state space, action space and reward function.

What is the optimal harvest policy for this problem and how can it be implemented

in practice? How does the policy change if the batch harvest CH are doubled, from

CH $=350$ to CH $=700?$ Why does the harvest policy change like this?