Part $1$:

Write code for a multi$-$arm bandit algorithm that has the following characteristics:

A: number of arms

P: Distribution of rewards $[0,1].$ Use the beta distribution so you can tune the rewards distribution based on two parameters. Choose your own parameter settings and graph the distributions in one plot.

r$\_$i: reward $(0$ or $1)$ taken from probability distribution P$\_$i

T: number of rounds played $($gambles$)$

R: calculate the regret $($difference between actual reward and reward if you played optimally$)$ as a function of time $($number of rounds T$)$

Part $2$:

Suppose you have $4$ arms $($A$=4).$ Implement a random and a greedy approach to selecting the best arm to play.

$**$ NEW QUESTION $**$

Using the same code as before, implement the epsilon$-$greedy, the Epsilon$-$first greedy, and the upper confidene bound $($UCB$1)$ approaches.