Use Cutting-Edge Reinforcement Learning algorithms in Environments like Flappy Bird, Mario, Stocks and Much More!!
What you will learn
☑ Practical Reinforcement Learning
☑ Master Open AI Gyms
☑ Flappy Bird Agent
☑ Mario Agent
☑ Stocks Agents
☑ Car Agents
☑ Space Invaders Agent
☑ and Much More!!
☑ Build Reinforcement Learning Agents in Any Environment
Description
Join the most comprehensive Reinforcement Learning course on Udemy and learn how to build Amazing Reinforcement Learning Applications!
Do you want to learn how to build cutting edge trading algorithms that leverage todays technology? Or do you want to learn the tools and skills that are considered the state of the art of Artificial Intelligence? Or do you just want to learn Reinforcement Learning in a Highly practical way?
After completing this course you will be able to:
- Build any reinforcement learning algorithm in any environment
- Use Reinforcement Learning for your own scientific experiments
- Solve problems using Reinforcement Learning
- Leverage Cutting Edge Technologies for your own project
- Master OpenAI gym’s
Why should you choose this course?
This course guides you through a step-by-step process of building state of the art trading algorithms and ensures that you walk away with the practical skills to build any reinforcement learning algorithm idea you have and implement it efficiently.
Here’s what’s included in the course:
- Atari Reinforcement Learning Agent
- Build Q-Learning from scratch and implement it in Autonomous Taxi Environment
- Build Deep Q-Learning from scratch and implement it in Flappy Bird
- Build Deep Q-Learning from scratch and implement it in Mario
- Build a Stock Reinforcement Learning Algorithm
- Build a intelligent car that can complete various environments
- And much more!
This course is for you if …
- You’re interested in cutting edge technology and applying it in practical ways
- You’re passionate about Deep Learning/AI
- Want to learn about cutting-edge technologies!
- Want to learn reinforcement learning by doing cool projects!
Course prerequisites:
- Python!
English
Language
Content
Introduction
Introduction
SpaceInvaders Agent
Setting up for Course & Installing Python Packages
Importing Packages & Setting up Gym Environment
Building Neural Network
Building Reinforcement Learning Agent
Training Agent
Visualizing our Agent
Save & Load Agent
Autonomous Taxi Agent
Setting up Project & Installing Python Packages
Importing Packages & Setting up Gym Environment
What is Q-Learning?
Implementing Q-Learning from Scratch
Training Q-Learning Agent
Analyzing our Trained Agent
Visualizing our Agent & Testing in other Environments
Flappy Bird Reinforcement Learning Agent
Setting up Project & Installing Python Packages
Importing Packages
Building class for Flappy Bird Agent
What is Deep Q Learning ?
Building Neural Network
Acting Function
Train Function
Learn Function
Visualize Function
Visualize Trained Agent
Mario Reinforcement Learning Agent
Setting up Project & Installing Python Packages
Importing Packages
Setting up Gym Environment
Building Class for Mario Agent
Building Neural Network
Act Function
Update Epsilon Function
Train Function
Preprocess State for Neural Network Function
Learn Function
Creating a Better Learning Environment for Agent
Save & Load Agent
Visualizing our Agent
Stocks Reinforcement Learning Agents
Setting up Project & Installing Python Packages
Importing Packages
Getting our S&P 500 Data
Preprocessing our Data
Creating our Environment
Taking Random Actions in Environment
Creating Model & Learning from Environment
Visualizing our Agent
PART 2: Implementing 89 different Technical Indicators in Data
PART 2: Creating Environment with Technical Indicators
PART 2: Visualizing our Agent in TA Environment
Car Reinforcement Learning Agents
Setting up Project & Installing Python Packages
Importing Packages
Agent 1) Setting up Roundabout Gym Environment
Agent 1) Training & Visualizing our Agent
Agent 2) Setting up Parking Gym Environment
Agent 2) Training our Agent
Agent 2) Visualizing our Agent
Agent 3) Highway Agent