Curiosity Driven Deep Reinforcement Learning

Curiosity Driven Deep Reinforcement Learning, available at $54.99, has an average rating of 4.7, with 27 lectures, based on 113 reviews, and has 1429 subscribers.

You will learn about How to Code A3C Agents How to Do Parallel Processing in Python How to Implement Deep Reinforcement Learning Papers How to Code the Intrinsic Curiosity Module This course is ideal for individuals who are This course is for advanced students of deep reinforcement learning It is particularly useful for This course is for advanced students of deep reinforcement learning.

Enroll now: Curiosity Driven Deep Reinforcement Learning

Summary

Title: Curiosity Driven Deep Reinforcement Learning

Price: $54.99

Average Rating: 4.7

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

How to Code A3C Agents

How to Do Parallel Processing in Python

How to Implement Deep Reinforcement Learning Papers

How to Code the Intrinsic Curiosity Module

Who Should Attend

This course is for advanced students of deep reinforcement learning

Target Audiences

This course is for advanced students of deep reinforcement learning

If reinforcement learning is to serve as a viable path to artificial general intelligence, it must learn to cope with environments with sparse or totally absent rewards. Most real life systems provided rewards that only occur after many time steps, leaving the agent with little information to build a successful policy on. Curiosity based reinforcement learning solves this problem by giving the agent an innate sense of curiosity about its world, enabling it to explore and learn successful policies for navigating the world.

In this advanced course on deep reinforcement learning, motivated students will learn how to implement cutting edge artificial intelligence research papers from scratch. This is a fast paced course for those that are experienced in coding up actor critic agents on their own. We’ll code up two papers in this course, using the popular PyTorch framework.

The first paper covers asynchronous methods for deep reinforcement learning; also known as the popular asynchronous advantage actor critic algorithm (A3C). Here students will discover a new framework for learning that doesn’t require a GPU. We will learn how to implement multithreading in Python and use that to train multiple actor critic agents in parallel. We will go beyond the basic implementation from the paper and implement a recent improvement to reinforcement learning known as generalized advantage estimation. We will test our agents in the Pong environment from the Open AI Gym’s Atari library,and achieve nearly world class performance in just a few hours.

From there, we move on to the heart of the course: learning in environments with sparse or totally absent rewards. This new paradigm leverages the agent’s curiosity about the environment as an intrinsic reward that motivates the agent to explore and learn generalizable skills. We’ll implement the intrinsic curiosity module (ICM),which is a bolt-on module for any deep reinforcement learning algorithm. We will train and test our agent in an maze like environment that only yields rewards when the agent reaches the objective. A clear performance gain over the vanilla A3C algorithm will be demonstrated, conclusively showing the power of curiosity driven deep reinforcement learning.

Please keep in mind this is a fast paced course for motivated and advanced students. There will be only a very brief review of the fundamental concepts of reinforcement learning and actor critic methods, and from there we will jump right into reading and implementing papers.

The beauty of both the ICM and asynchronous methods is that these paradigms can be applied to nearly any other reinforcement learning algorithm. Both are highly adaptable and can be plugged in with little modification to algorithms like proximal policy optimization, soft actor critic, or deep Q learning.

Students will learn how to:

Implement deep reinforcement learning papers

Leverage multi core CPUs with parallel processing in Python

Code the A3C algorithm from scratch

Code the ICM from first principles

Code generalized advantage estimation

Modify the Open AI Gym Atari Library

Write extensible modular code

This course is launching with the PyTorch implementation, with a Tensorflow 2 version coming.

I’ll see you on the inside.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What You Will Learn in this Course

Lecture 2: How to Succeed in this Course

Lecture 3: Required Background, Software, and Hardware

Chapter 2: Fundamental Concepts

Lecture 1: A Brief Review of Deep Reinforcement Learning and Actor Critic Methods

Lecture 2: Code Review of Basic Actor Critic Agent

Lecture 3: A Crash Course in Asynchronous Advantage Actor Critic Methods

Lecture 4: Our Code Structure

Chapter 3: Paper Analysis: Asynchronous Methods for Deep Reinforcement Learning

Lecture 1: How to Read and Implement Research Papers

Lecture 2: A3C Paper: Abstract and Introduction

Lecture 3: Crash Course in Parallel Processing in Python

Lecture 4: A3C Paper: Related Work, Reinforcement Learning Background

Lecture 5: A3C Paper: The Asynchronous Reinforcement Learning Framework

Lecture 6: Coding our Actor Critic Network

Lecture 7: Learning with Generalized Advantage Estimation

Lecture 8: Coding a Minimalist Replay Memory

Lecture 9: Coding the Shared Adam Optimizer

Lecture 10: A3C Paper: Experiments and Discussion

Lecture 11: How to Modify the Open AI Gym Atari Environments

Lecture 12: Coding Our Main Loop and Evaluating Our Agent

Chapter 4: Paper Analysis: Curiosity Driven Exploration by Self Supervised Prediction

Lecture 1: Paper Overview

Lecture 2: ICM Paper: Abstract and Introduction

Lecture 3: ICM Paper: Curiosity Driven Exploration

Lecture 4: Experimental Setup and Coding Our ICM Module

Lecture 5: ICM Paper: Experiments, Related Work, and Discussion

Lecture 6: Setting Up the Mini World and Training Our ICM Agent

Chapter 5: Appendix

Lecture 1: Setting Up Our Virtual Environment for the New Open AI Gym

Lecture 2: Making Our Agents Compliant with the New Gym Interface

Instructors

Phil Tabor
Machine Learning Engineer

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2 stars: 0 votes

3 stars: 4 votes

4 stars: 29 votes

5 stars: 80 votes

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