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


Subscribe to latest coupons on our Telegram channel.

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