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The post explains the basics of reinforcement learning using a Q-learning agent in Python through the example of Tic Tac Toe. It covers essential concepts like exploration vs. exploitation, policy, reward signal, value function, and state modeling. The tutorial demonstrates how to train an AI using Q-learning to optimize decision-making in a game scenario. It features a step-by-step guide, including coding examples, to illustrate the learning process and the agent's performance improvement over time.

Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python

How an RL agent thinks, decides — and learns

Exploitation vs. Exploration: Move 37 – And what we can learn from it