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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