Youtor
Deep QLearningDeep QNetwork DQN Explained Python Pytorch Deep Reinforcement Learning
YOUR LINK HERE:
http://youtube.com/watch?v=EUrWGTCGzlA
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. • Want more videos like this? Support me here: https://www.buymeacoffee.com/johnnycode • GitHub Repo: https://github.com/johnnycode8/gym_so... • Part 2 - Add Convolution Layers to DQN: • Convolutional Neural Network (CNN) in... • Reinforcement Learning Playlist: • Gymnasium (Deep) Reinforcement Learni... • Resources mentioned in video: • How to Solve FrozenLake-v1 with Q-Learning: • Q-Learning Tutorial 1: Train Gymnasiu... • Need help installing the Gymnasium library? • Install Gymnasium (OpenAI Gym) on Win... • Solve Neural Network in Python and by hand: • How to Calculate Loss, Backpropagatio... • • 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
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