How can I structure my Python coding for the implementation of the Q-learning?
I Currently developing reinforcement learning algorithms to train an autonomous agent for navigating a maze environment. Describe the steps for me of how can I structure my Python programming coding so that I can implement the Q-learning algorithms for this particular task.
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>