$)$ Consider a reinforcement learning agent operating in a grid$-$world environment. The agent receives a reward of $+10$ for reaching the goal state and a reward of $-1$ for each step taken. If the agent starts from a fixed position and can move in four possible directions $($up$,$ down, left, right$),$ explain how the agent's exploration strategy might impact its learning efficiency and the time taken to reach the optimal policy. Provide examples of two different exploration strategies. $[2$ Marks$]$

B$)$ Analyze the impact of different discount factors on the leaming process of a reinforcement learning agent. Explain how the discount factor $($y$)$ influences the agent's ability to balance immediate rewards versus long$-$term rewards. Provide examples of two extreme cases of discount factors and discuss their effects on the agent's behavior and learning efficiency.