The Ultimate Ai & Reinforcement Learning Training Course

This course teaches you to learn reinforcement learning at your own pace to develop your own intelligent applications.

What you'll learn
Reinforcement Learning Basics
Understand the motivation for reinforcement learning
Learn how to manage and install software for machine
Learn how to implement common RL algorithms
Learn to Generate a Random MDP Problem
Learn how to solve various reinforcement learning problems
Learn how to model uncertainty of the environments
Solve Markov Decision Processes
Requirements
Basic familiarity with linear algebra, calculus, and the Python programming language is helpful
Description
Welcome to this course. Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. It is a part of machine learning. Reinforcement learning is one powerful paradigm for making good decisions, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. Building on a strong theoretical foundation, this course takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.  Reinforcement learning allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance.Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package.In this course, you'll learnReinforcement Learning BasicsUnderstand the motivation for reinforcement learningLearn how to manage and install software for machineLearn how to implement common RL algorithmsLearn to Generate a Random MDP ProblemLearn how to solve various reinforcement learning problemsLearn how to model uncertainty of the environmentsSolve Markov Decision ProcessesExecute the Frozenlake project using the OpenAI Gym toolkitBy the end of this course, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

Overview

Section 1: Welcome

Lecture 1 Introduction

Section 2: Getting started

Lecture 2 Introduction

Lecture 3 Learn About Anaconda Virtual Environments

Lecture 4 Learn About OpenAI Gym

Lecture 5 Reinforcement Learning Basics

Lecture 6 Understanding Terminology

Lecture 7 OpenAI Gym Basics

Lecture 8 Understanding Environments

Section 3: Learning Key Concepts

Lecture 9 Markov Decision Processes

Lecture 10 Solving Markov Decision Processes

Lecture 11 Determine a Reinforcement Learning Problem

Section 4: Simple Reinforcement Learning Problems

Lecture 12 Solving Taxi Environment

Lecture 13 Solving Frozen Lake Environment

Lecture 14 Reward Discounting

Lecture 15 Section Summary

Section 5: Deep Reinforcement Learning

Lecture 16 Introduction

Lecture 17 Solving Mountain Car Environment

Lecture 18 Solving Pong Atari Game With TensorFlow

Section 6: Course Summary

Lecture 19 Summary

Section 7: Course Material & Source Code

Lecture 20 Course Material & Source Code

Beginners in the field of data science and machine learning,Anyone who wants to learn RL