Using reinforcement learning from scratch to show a pc to play Tic-Tac-Toe
It seems that everyone within the AI sector is currently honing their Reinforcement Learning (RL) skills, especially in Q-learning, following the recent rumours about OpenAI’s recent AI model, Q* and I’m joining in too. Nonetheless, fairly than speculating about Q* or revisiting old papers and examples for Q-learning, I’ve decided to make use of my enthusiasm for board games to offer an introduction to Q-learning 🤓
On this blog post, I’ll create a straightforward programme from scratch to show a model learn how to play Tic-Tac-Toe (TTT). I’ll refrain from using any RL libraries like Gym or Stable Baselines; all the pieces is hand-coded in native Python, and the script is merely 100 lines long. Should you’re interested in learn how to instruct an AI to play games, keep reading.
You’ll find all of the code on GitHub at https://github.com/marshmellow77/tictactoe-q.
Teaching an AI to play Tic-Tac-Toe (TTT) won’t seem all that vital. Nonetheless, it does provide a (hopefully) clear and comprehensible introduction to Q-learning and RL, which could be vital in the sector of Generative AI (GenAI) since there was speculation that stand-alone GenAI models, equivalent to GPT-4, are insufficient for significant advancements. They’re limited by the incontrovertible fact that they will only ever predict the following token and never with the ability to reason in any respect. RL is believed to give you the option to deal with this issue and potentially enhance the responses from GenAI models.
But whether you’re aiming to brush up in your RL skills in anticipation of those advancements, otherwise you’re simply searching for an enticing introduction to Q-learning, this tutorial is designed for each scenarios 🤗
At its core, Q-learning is an algorithm that learns the worth of an motion in a selected state, after which uses this information to seek out the most effective motion. Let’s consider the instance of the Frozen Lake game, a well-liked single-player game used to reveal Q-learning.