Tic Tac Toe

A tale about trying to train a machine to play Tic Tac Toe through Reinforcement Learning

To run the Jupyter notebooks in Binder press:

The goal of this series is to implement and test a couple of different approaches to training a computer how to play Tic Tac Toe. We will create:

A player that plays completely randomly,
Two players that implement simple forms of the Min-Max algorithm,
Several players that we will train through Reinforcement Learning:
- a Tabular Q-Learning player.
- a Simple Neural Network Q-Learning player.
- a Deep Neural Network Q-learning player.
- a Policy Gradient Descent based player.

I assume you are familiar with:

The rules and basic strategy of playing Tic Tac Toe.
Basic Python 3 programming and use of a Python IDE or Jupyter Notebooks.
At least rudimentary knowledge of Tensorflow and Neural Networks would be helpful, but you might be able to do without (give it a try and if it's too overwhelming do some of the beginner tutorials, and then try again).

Name		Name	Last commit message	Last commit date
Latest commit History 107 Commits
Images		Images
pre-rendered-notebooks		pre-rendered-notebooks
tic_tac_toe		tic_tac_toe
.gitignore		.gitignore
LICENSE		LICENSE
Part 1 - Computer Tic Tac Toe Basics.ipynb		Part 1 - Computer Tic Tac Toe Basics.ipynb
Part 2 - The Min Max Algorithm.ipynb		Part 2 - The Min Max Algorithm.ipynb
Part 3 - Tabular Q-Learning.ipynb		Part 3 - Tabular Q-Learning.ipynb
Part 4 - Neural Network Q-Learning.ipynb		Part 4 - Neural Network Q-Learning.ipynb
Part 5 - Q Network review and becoming less greedy.ipynb		Part 5 - Q Network review and becoming less greedy.ipynb
Part 6 - Double Duelling Q Network with Experience Replay.ipynb		Part 6 - Double Duelling Q Network with Experience Replay.ipynb
Part 7 - This is deep. In a convoluted way.ipynb		Part 7 - This is deep. In a convoluted way.ipynb
Part 8 - Tic Tac Toe with Policy Gradient Descent.ipynb		Part 8 - Tic Tac Toe with Policy Gradient Descent.ipynb
Part 9 - Using TensorBoard.ipynb		Part 9 - Using TensorBoard.ipynb
README.md		README.md
index.ipynb		index.ipynb
requirements.txt		requirements.txt
reward_sweep.py		reward_sweep.py
test.py		test.py
test_nn_q.py		test_nn_q.py
test_part4.py		test_part4.py
test_part5.py		test_part5.py
test_part6.py		test_part6.py
test_part7.py		test_part7.py
testq.py		testq.py
util.py		util.py