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PYTHON · REINFORCEMENT LEARNING

Q-Learning Tic-Tac-Toe Agent

A graduate artificial-intelligence project demonstrating how an agent can improve its decisions through rewards, penalties, and repeated play.

Q-learning Tic-Tac-Toe agent visualization
Portfolio visualization recreated with sample or illustrative data to demonstrate the project’s analytical approach.

OBJECTIVE

Build an agent that learns a stronger Tic-Tac-Toe policy through experience rather than relying only on hard-coded move rules.

APPROACH

Represented game states and actions, assigned rewards for wins, losses, and draws, updated Q-values through repeated episodes, and evaluated how the learned policy changed with training.

ANALYTICAL VALUE

Demonstrated the core mechanics of reinforcement learning: state-action representation, exploration versus exploitation, reward design, and iterative policy improvement.

SKILLS SHOWN

Reinforcement learning, algorithm design, Python programming, experimentation, state representation, and performance evaluation.

TOOLS

PythonQ-LearningReinforcement LearningAlgorithm Design
Visualization note: The visual above is a portfolio reconstruction based on the project methods and deliverables. It is provided to demonstrate the analysis without reproducing restricted course or organizational materials.