a$)$ Consider again the RL agent learning the game of tic$-$tac$-$toe by playing against different randomly chosen opponents. It is decided that the learning algorithm does not use explicit exploratory moves? under this scenario, would you consider RL agent to learn the same as supervised learning? $($one word, yes$/$No whose marking depends only on the correct explanation$]$

Explain in two well articulated statements. $[$caution: your third and further statements for explanation will not be evaluated$].$

b$)$ State one situation I$\text{'}$m which an greedy action selection works better than E$-$ greedy action selection.

c$)$ write any one use case $($application$)$ from your workplace which can be modeled as a multi$-$armed bandit for Its solution. your answer should have all the elements that identify the use case and can be modeled as a multi$-$armed bandit problem.