427.427. A2C Algorithm

A2C Algorithm

==============

In this problem, you will implement the A2C (Advantage Actor-Critic) algorithm, a popular reinforcement learning algorithm that combines the benefits of policy-based and value-based methods.

**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 A2C algorithm with a simple neural network policy and a value function.

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