How can I structure my Python coding for the implementation of the Q-learning?

In the context of data science, here is the structure given for your Python code to implement the Q-learning algorithms for training an autonomous agent to navigate a maze environment:-

Import numpy as np

Class MazeEnvironment:
    Def __init__(self, maze_size):
        Self.maze_size = maze_size
        Self.state = (0, 0)  # Initial state
        Self.goal_state = (maze_size – 1, maze_size – 1)  # Goal state
        Self.actions = [‘up’, ‘down’, ‘left’, ‘right’]  # Possible actions
        Self.q_table = np.zeros((maze_size, maze_size, len(self.actions)))  # Q-table
    Def take_action(self, action):
        If action == ‘up’ and self.state[0] > 0:
            Self.state = (self.state[0] – 1, self.state[1])
        Elif action == ‘down’ and self.state[0] < self xss=removed xss=removed> 0:
            Self.state = (self.state[0], self.state[1] – 1)
        Elif action == ‘right’ and self.state[1] < self xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed>