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Implementation of Q-learning Algorithm for Reinforcement Learning Task

import numpy as np # Define the environment (grid world) grid_world = np.array([ [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, 1] # Goal state ]) # Define the Q-table Q = np.zeros((4, 4, 4)) # Q-table with dimensions (rows, cols, actions) # Define parameters alpha = 0.1 # Learning rate gamma = 0.9 # Discount factor epsilon = 0.1 # Exploration rate # Define actions (0: up, 1: down, 2: left, 3: right) actions = [0, 1, 2, 3] # Perform Q-learning num_episodes = 1000 for _ in range(num_episodes): state = (0, 0) # Initial state while state != (3, 3): # Continue until goal state is reached if np.random.uniform(0, 1) < epsilon: action = np.random.choice(actions) # Exploration else: action = np.argmax(Q[state[0], state[1]]) # Exploitation next_state = (state[0] + (action == 0) - (action == 1), state[1] + (action == 3) - (action == 2)) reward = grid_world[next_state[0], next_state[1]] Q[state[0], state[1], action] += alpha * (reward + gamma * np.max(Q[next_state[0], next_state[1]]) - Q[state[0], state[1], action]) state = next_state # Optimal policy extraction optimal_policy = np.argmax(Q, axis=2) print("Optimal Policy:") print(optimal_policy)

This code implements the Q-learning algorithm to learn the optimal policy for navigating a simple grid world with a goal state. The student can study this code to understand the basics of Q-learning and how it can be applied to solve reinforcement learning problems. By experimenting with different parameters and environments, the student can gain practical experience and insight into the workings of reinforcement learning algorithms. This hands-on approach will help reinforce theoretical concepts and deepen the student's understanding of the subject.

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