Play to Learn: Tic-Tac-Toe Using Reinforcement Learning his project explores the application of reinforcement learning, specifically the Q-learning algorithm, to develop an intelligent Tic-Tac-Toe game. The game is implemented in a console-based environment where a human player competes against an AI agent trained to make optimal moves.Through self-play and reward-based learning, the AI agent develops strategies to enhance its performance over time. Tic-Tac-Toe, a simple yet strategic game, serves as an excellent platform for experimenting with artificial intelligence (AI) and machine learning (ML) concepts. This project leverages the Q-learning algorithm, a model-free reinforcement learning technique, to train an AI agent capable of playing Tic-Tac-Toe optimally. Unlike traditional hard-coded AI strategies, this approach allows the agent to learn and improve through experience without explicit programming of strategies. Features Interactive Gameplay: Play Tic-Tac-Toe against an AI agent in a console-based environment.

Reinforcement Learning Integration: The AI agent utilizes the Q-learning algorithm to learn optimal strategies through self-play.

Adaptive Strategy Development: Over multiple training episodes, the AI agent refines its decision-making policy based on received rewards.