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

Показать описание

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.

Resources mentioned in video:

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

Johnny Code

Рекомендации по теме

Комментарии

Please like and subscribe if this video is helpful for you 😀

johnnycode

The best teachers are those who teach a difficult lesson simply. thank you

zwpdioe

Johnny, you explained what I've been trying to wrap my head around for 9 months in a few minutes. Keep up the good work.

bagumamartin

Thank you for making this video. Your explanations were clear👍, and I learned a lot. Also, I find your voice very pleasant to listen to.

thefall

This was a really well explained example

johndowling

These videos on the new gymnasium version of gym are great. ❤ Could you do a video about thr bipedal walker environment?

johngrigoriadis

Thanks for this tutorial. It helped me to understand DQN .

rhbnzcq

You are a great teacher, thank you so much and Merry Christmas 2023

drm

Thanks again for your good work to help me understand reinforcement learning better.

kimiochang

thank you so much.can u please implement any env with dqn showing the forgetting problem?

koka-lfui

how to start learning reinforcement learning? i knew panda numpy matplotlib and basic ml algo?

DEVRAJ-npog

merci d'avoir dit moi comment crier l'environement en preier lieu, puisque je voudrais crier un environement tels que votre 4 x 4 mais avec d'autre images .

kskcqzd

Hey Johnny! Thanks so much for these videos! I have a question, is it possible to apply this algorithm to a continuous action space? For example, select a number in a range between [0, 120] as an action, or should I investigate other algorithms?

rickyolal

Thank you for making this video, may I ask a question about the Q network, why you set the input space for the network 16 input at 5:19 rather than 1 that represents the state index only?
I think 1 input is enough

nimo

sry, may i ask how can i find the max_step in the training of every epsiode? i do i know that max action is 200?

envelopepiano

Thanks for the video! Can you make a example with a BattleShips game? im trying, but the action (ex. position 12) its the same that the new state (12)😢

ProjectOfTheWeek

Great video, thanks. Please I am from cyber security background, please do you have idea on Network Attack Simulator (NASim) which also uses Deep Q-learning and openai gym? If you don't, please can you guide on where to find tutorials on it? I have checked youtube for weeks but couldn't get any. THANKS

AfitQADirectorate

I don't quite understand, because if you change the position of the puddles, the trained model will no longer be able to find the reward, right? What is the purpose of Qlearning then?

ProjectOfTheWeek

Any chance you can show us how to use Keras and RL on Tetris?

fernandomaroli

Just curious how you learned all of this? Did you just read the documentation or watch other videos?

codelock

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

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

Deep Q-Networks Explained!

Deep Q-Learning - Combining Neural Networks and Reinforcement Learning

Deep Q Learning w/ DQN - Reinforcement Learning p.5

Deep Q-Network & Dueling network architectures for deep reinforcement learning

Deep Q-Networks (DQNs) in Reinforcement Learning

Overview of Deep Reinforcement Learning Methods

Deep Q-Network (DQN)

DQN (Deep Q-Network) theory and implementation. Using DQN with ROS and Gazebo.

Deep Q Learning is Simple with PyTorch | Full Tutorial 2020

Replay Memory Explained - Experience for Deep Q-Network Training

Deep Q-Learning paper explained: Human-level control through deep reinforcement learning (algorithm)

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

What is Q-Learning (back to basics)

Deep Q Learning Networks

DQN - Playing Atari with Deep Reinforcement Learning | RL Paper Explained

Double Q-Learning | Lecture 81 (Part 1) | Applied Deep Learning

2) Deep Q Network DQN

Q Learning Explained (tutorial)

Deep Q-Networking (DQN)

Training a Deep Q-Network - Reinforcement Learning

MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)

A friendly introduction to deep reinforcement learning, Q-networks and policy gradients

MIT 6.S191 (2023): Reinforcement Learning