426.426. Advantage Function

Advantage Function

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In this problem, you will implement the advantage function, a key concept in reinforcement learning that measures the difference between the expected return and the actual return.

**Example:** Consider a simple grid world where an agent can move up, down, left, or right. The agent receives a reward of +1 for reaching the goal and -1 for hitting a wall.

**Constraints:** Use NumPy for efficient computation and implement the advantage function with a simple neural network policy and a value function.

**Note:** This problem requires a good understanding of reinforcement learning concepts and NumPy.