An agent's performance is evaluated using a function $J(\theta)$, which depends on a set of parameters $\theta$. The goal is to find the optimal parameters, $\tilde{\theta}$, that maximize this function. The table below shows the performance values for four different sets of parameters. Which of the following represents the optimal parameters, $\tilde{\theta}$?

A reinforcement learning agent's policy is defined by a set of parameters, $\theta$. After extensive training, it is determined that the performance function, $J(\theta)$, reaches its peak value of 450 when the parameters are set to $\theta = \{'learning\_rate': 0.01, 'discount\_factor': 0.95\}$. According to the optimization objective $\tilde{\theta} = \underset{\theta}{\arg\max} \, J(\theta)$, what does $\tilde{\theta}$ represent in this scenario?

Distinguishing Maximum Value from Optimal Argument