Implementation of Q-learning using n*n matrix

Here is the portal for uploading your HW: Implementation of Q-learning. A short rubric: Total points : 20 - Correct initialization ( proper n*n Q-matrix, R matrix or vector, etc. according to your implementation ): 3 points - Correct transition function or matrix to get the next state given the current state and the action: 3 points - Correct function or code block for choosing a random and valid action, or similar. 3 points - Implement episode iterations, calculate q value and update q matrix correctly : 6 points - Return the correct path of reaching the goal state given Q matrix : 5 points (this means you need to create a concrete gridworld using your implementation and find the solution) Ps: you can set learning rate alpha equal to 1 so as to use the simplest form of q equation : Q(s,a) <-- R(s,a) + gamma* max ( Q'(s',a')) in the homework. Extra Credit: - Show the update of q matrix every N episodes ( You choose N ) : 1 points - Set alpha between (0,1) : 2 points - Implement a simple GUI which shows the movement of agent or the change of policy: 2 points