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Deep Reinforcement Learning Using Python

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Published 1/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.75 GB | Duration: 9h 7m

Complete guide to deep reinforcement learning

What you’ll learn
Understand deep reinforcement learning and its applications
Build your own neural network
Implement 5 different reinforcement learning projects
Learn a lot of ways to improve your robot

Requirements
Numpy, Matplotlib ,Pandas
Gradient descent
object-oriented programming
General understanding of deep learning

Description
Welcome to Deep Reinforcement Learning using python!Have you ever asked yourself how smart robots are created?Reinforcement learning concerned with creating intelligent robots which is a sub-field of machine learning that achieved impressive results in the recent years where now we can build robots that can beat humans in very hard games like alpha-go game and chess game.Deep Reinforcement Learning means Reinforcement learning field plus deep learning field where deep learning it is also a a sub-field of machine learning which uses special algorithms called neural networks.In this course we will talk about Deep Reinforcement Learning and we will talk about the following things :-Section 1: An Introduction to Deep Reinforcement LearningIn this section we will study all the fundamentals of deep reinforcement learning . These include Policy , Value function , Q function and neural network.Section 2: Setting up the environmentIn this section we will learn how to create our virtual environment and installing all required packages.Section 3: Grid World Game & Deep Q-LearningIn this section we will learn how to build our first smart robot to solve Grid World Game.Here we will learn how to build and train our neural network and how to make exploration and exploitation.Section 4: Mountain Car game & Deep Q-LearningIn this section we will try to build a robot to solve Mountain Car game.Here we will learn how to build ICM module and RND module to solve sparse reward problem in Mountain Car game.Section 5: Flappy bird game & Deep Q-learningIn this section we will learn how to build a smart robot to solve Flappy bird game.Here we will learn how to build many variants of Q network like dueling Q network , prioritized Q network and 2 steps Q networkSection 6: Ms Pacman game & Deep Q-LearningIn this section we will learn how to build a smart robot to solve Ms Pacman game.Here we will learn how to build another variants of Q network like noisy Q network , double Q network and n-steps Q network.Section 7:Stock trading & Deep Q-LearningIn this section we will learn how to build a smart robot for stock trading.

Overview
Section 1: An Introduction to Deep Reinforcement Learning

Lecture 1 What is reinforcement learning?

Lecture 2 Policy , Value function and Q function

Lecture 3 What are Neural Networks?

Lecture 4 Optimal Q function

Section 2: Setting up the environment

Lecture 5 creating anaconda environment

Lecture 6 Gym package

Lecture 7 How to run the code of each section

Section 3: Grid World Game & Deep Q-Learning

Lecture 8 What is Grid World Game?

Lecture 9 How to use Grid World environment ?

Lecture 10 How to build your network ?

Lecture 11 How to Build your first Q network using pytorch ?

Lecture 12 How to make your neural network learn ?

Lecture 13 Exploration & Exploitation using epsilon greedy

Lecture 14 Training your neural network using pytorch part1

Lecture 15 Training your neural network using pytorch part2

Lecture 16 Batch training

Lecture 17 train on batches python code

Lecture 18 reward metric

Lecture 19 Target nework

Lecture 20 train your agent with target network python code

Section 4: Mountain Car game & Deep Q-Learning

Lecture 21 Mountain car in python

Lecture 22 Dynamics network

Lecture 23 Epsilon Greedy strategy mountain Car game in python

Lecture 24 Dynamics Network with python

Lecture 25 Multi variate gaussian distribution

Lecture 26 Multivariate gaussian distribution with python

Lecture 27 Model based exploration strategy with mountain car in python

Lecture 28 What is ICM module ?

Lecture 29 Filter network

Lecture 30 Building Filter net python code

Lecture 31 Inverse network

Lecture 32 Building Inverse net python code

Lecture 33 Forward network

Lecture 34 Building Forward network python code

Lecture 35 Building Agent Q network & Target Q network python code

Lecture 36 Training Q network with ICM

Lecture 37 Training Agent Q network with ICM python code

Lecture 38 What is RND module?

Lecture 39 Building P net & T net python code

Lecture 40 Training Agent Q network with RND module

Section 5: Flappy bird game & Deep Q-learning

Lecture 41 Flappy bird game

Lecture 42 Flappy bird game python code

Lecture 43 Building convolution Q network

Lecture 44 conv Q network with epsilon greedy approach python code

Lecture 45 2-steps Q network

Lecture 46 2-steps Q network python code

Lecture 47 Prioritized Experience Replay buffer

Lecture 48 Prioritized Experience Replay buffer python code

Lecture 49 Dueling Q network

Lecture 50 Dueling Q network python code

Section 6: Ms Pacman game & Deep Q-Learning

Lecture 51 Ms Pacman game

Lecture 52 Ms Pacman game python code

Lecture 53 Basic Q network python code

Lecture 54 N-steps Q network

Lecture 55 N-steps Q network python code

Lecture 56 Noisy Q network

Lecture 57 Noisy Q network python code

Lecture 58 Noisy double dueling Q network python code

Section 7: Stock trading & Deep Q-Learning

Lecture 59 Basics of Trading

Lecture 60 Stock Data Preprocessing

Lecture 61 Building the trading environment

Lecture 62 Building dueling conv1d Q network

Lecture 63 Train your trading robot

Anyone who wants to learn about artificial intelligence and deep learning,students & professionals


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