AI learns to balance a stick using Reinforcement Learning

In this video, I show step by step how I made a algorithm to learn from basic left right input how to make an agent balance a stick. This was done using the Gym environment from openAI and with Q-learning!

A good resource for Q-learning is this blogpost: https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/
You can take a peak at my code here : https://github.com/yacineMahdid/ReinforcementLearning/tree/master/Cartpole

Q learning is defined as follow by wikipedia:
“Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence “model-free”), and it can handle problems with stochastic transitions and rewards without requiring adaptations. ”

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