It seems like everyone in the ai industry is honing their reinforcement learning (RL) skills, especially in Q-learning, following recent rumors about OpenAI's new ai model. P* and I join too. However, instead of speculating about P* or reviewing old Q-learning articles and examples, I have decided to use my enthusiasm for board games to give an introduction to Q-learning
In this blog post, I will create a simple program from scratch to teach a model how to play Tic-Tac-Toe (TTT). I will refrain from using RL libraries like Gym either Stable baselines; everything is hand-coded in native Python and the script is only 100 lines. If you're curious about how to instruct an ai to play, read on.
You can find all the code on GitHub at https://github.com/marshmellow77/tictactoe-q.
Teaching an ai to play Tic-Tac-Toe (TTT) may not seem that important. However, it does provide a (hopefully) clear and understandable introduction to Q-learning and RL, which could be important in the field of generative ai (GenAI), as it has been speculated that standalone GenAI models, such as GPT-4 . , are insufficient for significant advances. They are limited by the fact that they can only predict the next token and cannot reason at all. It is believed that RL can address this problem and potentially improve the responses of GenAI models.
But whether your goal is to brush up on your RL skills in anticipation of these advances, or you're simply looking for an interesting introduction to Q-learning, this tutorial is designed for both scenarios
In essence, Q-learning is an algorithm that learns the value of an action in a particular state and then uses this information to find the best action. Let us consider the example of the Frozen lake game, a popular single-player game used to demonstrate Q-learning.
