Скачать с ютуб Detailed Explanation and Python Implementation of Q-Learning Algorithm in OpenAI Gym (Cart-Pole) в хорошем качестве

Detailed Explanation and Python Implementation of Q-Learning Algorithm in OpenAI Gym (Cart-Pole)

#machinelearning #reinforcementlearning #openaigym #controltheory #controlengineering #qlearning #deeplearning #deeplearningproject #robotics #openai #openaigym #datascience It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way: - Buy me a Coffee: https://www.buymeacoffee.com/Aleksand... - PayPal: https://www.paypal.me/AleksandarHaber - Patreon: https://www.patreon.com/user?u=320801... - You Can also press the Thanks YouTube Dollar button The webpage tutorial accompanying this video tutorial is given here: https://aleksandarhaber.com/q-learnin... In this reinforcement learning tutorial, we provide a detailed explanation of the Q-learning algorithm. The Q-learning algorithm is one of the most fundamental algorithms in reinforcement learning. First, we explain the basics of the Q-learning algorithm. Then, we explain how to implement this algorithm in Python from scratch. We also explain how to test this algorithm in the OpenAI Gym environment. We use the Cart Pole OpenAI Gym environment to test the method.