Deep Q-Learning/Deep Q-Network (DQN) Explained | Python Pytorch Deep Reinforcement Learning

This tutorial contains step by step explanation, code walkthru, and demo of how Deep Q-Learning (DQL) works. We'll use DQL to solve the very simple Gymnasium FrozenLake-v1 Reinforcement Learning environment. We'll cover the differences between Q-Learning vs DQL, the Epsilon-Greedy Policy, the Policy Deep Q-Network (DQN), the Target DQN, and Experience Replay. After this video, you will understand DQL. Jump to my full walkthru of PPO: https://youtu.be/5VHLd9eCZ-w Want more videos like this? Support me here: https://www.buymeacoffee.com/johnnycode GitHub Repo: https://github.com/johnnycode8/gym_solutions Reinforcement Learning Playlist: https://www.youtube.com/playlist?list=PL58zEckBH8fCt_lYkmayZoR9XfDCW9hte Resources mentioned in video: How to Solve FrozenLake-v1 with Q-Learning: https://youtu.be/ZhoIgo3qqLU Need help installing the Gymnasium library? https://youtu.be/gMgj4pSHLww Solve Neural Network in Python and by hand: https://youtu.be/6kOvmZDEMdc 00:00 Video Content 01:09 Frozen Lake Environment 02:16 Why Reinforcement Learning? 03:12 Epsilon-Greedy Policy 03:55 Q-Table vs Deep Q-Network 06:51 Training the Q-Table 10:10 Training the Deep Q-Network 14:49 Experience Replay 16:03 Deep Q-Learning Code Walkthru 29:49 Run Training Code & Demo