Numerical Input

$1\mathrm{.}0/1\mathrm{.}0$ point $($graded$)$

Assume the agent uses REINFORCE for learning a policy while navigating in a continuous $2$D square maze, with center at origin. It starts at the state $.$ The agent's policy is parameterized by a linear function where the final layer outputs the mean action $.$ Here, is a $2$x$2$ matrix initialized as all zeros, and is the state. During execution, the agent then samples an action $,$ a $2-$dimensional Gaussian distribution with mean and identity variance. The first trajectory is: $.$ The trajectory ends in because the agent falls into a trap and receives a negative reward of $(.$ Otherwise, the agent receives a reward of for every previous step. Assume $.$

What is the return at state $?$ Please specify to the $4$th decimal place.