Deep Reinforcement Learning using python

Deep Reinforcement Learning using python, available at $64.99, has an average rating of 4.06, with 63 lectures, 31 quizzes, based on 9 reviews, and has 1173 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to learn about artificial intelligence and deep learning or students & professionals It is particularly useful for Anyone who wants to learn about artificial intelligence and deep learning or students & professionals.

Enroll now: Deep Reinforcement Learning using python

Summary

Title: Deep Reinforcement Learning using python

Price: $64.99

Average Rating: 4.06

Number of Lectures: 63

Number of Quizzes: 31

Number of Published Lectures: 63

Number of Published Quizzes: 31

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 94

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 Learning

In 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 environment

In this section we will learn how to create our virtual environment and installing all required packages.
Section 3: Grid World Game & Deep Q-Learning

In 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-Learning

In 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-learning

In 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 network
Section 6: Ms Pacman game & Deep Q-Learning

In 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-Learning

In this section we will learn how to build a smart robot for stock trading.

Course Curriculum

Chapter 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

Chapter 2: Setting up the environment

Lecture 1: creating anaconda environment

Lecture 2: Gym package

Lecture 3: How to run the code of each section

Chapter 3: Grid World Game & Deep Q-Learning

Lecture 1: What is Grid World Game?

Lecture 2: How to use Grid World environment ?

Lecture 3: How to build your network ?

Lecture 4: How to Build your first Q network using pytorch ?

Lecture 5: How to make your neural network learn ?

Lecture 6: Exploration & Exploitation using epsilon greedy

Lecture 7: Training your neural network using pytorch part1

Lecture 8: Training your neural network using pytorch part2

Lecture 9: Batch training

Lecture 10: train on batches python code

Lecture 11: reward metric

Lecture 12: Target nework

Lecture 13: train your agent with target network python code

Chapter 4: Mountain Car game & Deep Q-Learning

Lecture 1: Mountain car in python

Lecture 2: Dynamics network

Lecture 3: Epsilon Greedy strategy mountain Car game in python

Lecture 4: Dynamics Network with python

Lecture 5: Multi variate gaussian distribution

Lecture 6: Multivariate gaussian distribution with python

Lecture 7: Model based exploration strategy with mountain car in python

Lecture 8: What is ICM module ?

Lecture 9: Filter network

Lecture 10: Building Filter net python code

Lecture 11: Inverse network

Lecture 12: Building Inverse net python code

Lecture 13: Forward network

Lecture 14: Building Forward network python code

Lecture 15: Building Agent Q network & Target Q network python code

Lecture 16: Training Q network with ICM

Lecture 17: Training Agent Q network with ICM python code

Lecture 18: What is RND module?

Lecture 19: Building P net & T net python code

Lecture 20: Training Agent Q network with RND module

Chapter 5: Flappy bird game & Deep Q-learning

Lecture 1: Flappy bird game

Lecture 2: Flappy bird game python code

Lecture 3: Building convolution Q network

Lecture 4: conv Q network with epsilon greedy approach python code

Lecture 5: 2-steps Q network

Lecture 6: 2-steps Q network python code

Lecture 7: Prioritized Experience Replay buffer

Lecture 8: Prioritized Experience Replay buffer python code

Lecture 9: Dueling Q network

Lecture 10: Dueling Q network python code

Chapter 6: Ms Pacman game & Deep Q-Learning

Lecture 1: Ms Pacman game

Lecture 2: Ms Pacman game python code

Lecture 3: Basic Q network python code

Lecture 4: N-steps Q network

Lecture 5: N-steps Q network python code

Lecture 6: Noisy Q network

Lecture 7: Noisy Q network python code

Lecture 8: Noisy double dueling Q network python code

Chapter 7: Stock trading & Deep Q-Learning

Lecture 1: Basics of Trading

Lecture 2: Stock Data Preprocessing

Lecture 3: Building the trading environment

Lecture 4: Building dueling conv1d Q network

Instructors

Riad Almadani
Machine Learning Engineer

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 3 votes
5 stars: 4 votes

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