Q-learning is a popular reinforcement learning algorithm used to make decisions in an environment. It enables an agent to learn optimal actions by iteratively updating its Q-values, which represent the expected rewards for taking certain actions in specific states. Here is a step-by-step implementation of Q-learning using Python: Image by Author 1. Import the necessary libraries: import numpy as np import random 2. Define the environment: # Define the environment env = np.array([[ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0