Udacity – Deep Reinforcement Learning v1.0.0 2020-4

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Apr 17, 2025 - 07:05 Updated: Apr 20, 2025 - 17:26

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Udacity – Deep Reinforcement Learning v1.0.0 2020-4

Become a Deep Reinforcement Learning Expert is an in-depth reinforcement learning course published by Udacity Academy. Deep reinforcement learning is one of the technologies related to artificial intelligence and deep learning that has been used in a wide range of industries, the most important of which are video games and robotics. Among the most important algorithms of this technique are DQN (Deep Q-Networks) and DDPG (Deep Deterministic Policy Gradients) which are used in various projects such as self-propelled vehicles. With the help of these algorithms, various operators and neural networks can be trained to perform very complex and time-consuming tasks with high accuracy. At the end of this training course, you will build a complete portfolio and be ready to enter the job market.

Apple, Google, and Facebook are among the most important companies that have made countless advances in investing in deep learning algorithms. This course is an advanced and highly specialized course, and before starting this course, students should have a relative mastery of topics such as the basics of Python programming, statistics and probability, machine learning and deep learning, and so on.

What you will learn in Become a Deep Reinforcement Learning Expert

Principles and foundations of reinforcement learning
Architecture and development patterns of deep learning systems
Receive, store and interpret telecommunication data
Evolutionary algorithm
Design and development of advanced algorithms to train and practice data to simulated and virtual robots

Course specifications

Publisher: Udacity
Instructors: Alexis Cook, Arpan Chakraborty, Mat Leonard, Luis Serrano, Cezanne Camacho, Dana Sheahan, Chhavi Yadav, Juan Delgado and Miguel Morales
Language: English
Level: Advanced
Number of Lessons: 42
Duration: Approx. 4 Months

Course topics

Part 01 : Introduction to Deep Reinforcement Learning

Module 01: Introduction to Deep Reinforcement Learning

Part 02 : Value-Based Methods

Module 01: Value-Based Methods

Module 02: Career Services

Part 03 : Policy-Based Methods

Module 01: Policy-Based Methods

Module 02: Career Services

Part 04 : Multi-Agent Reinforcement Learning

Module 01: Multi-Agent Reinforcement Learning

Part 05 (Elective): Special Topics in Deep Reinforcement Learning

Module 01: Special Topics in Deep Reinforcement Learning

Part 06 (Elective): Neural Networks in PyTorch

Module 01: Neural Networks in PyTorch

Part 07 (Elective): Computing Resources

Module 01: Computing Resources

Part 08 (Elective): C++ Programming

Module 01: C++ Basics

Module 02: Performance Programming in C++

Become a Deep Reinforcement Learning Expert Prerequisites

What are the prerequisites for enrollment?
We recommend that you complete a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. You will need to be able to communicate fluently and professionally in written and spoken English.
Additionally, you should have the following knowledge:

Intermediate Python programming knowledge, including:

Strings, numbers, and variables Statements, operators, and expressions Lists, tuples, and dictionaries Conditions, loops Generators & comprehensions Procedures, objects, modules, and libraries Troubleshooting and debugging Research & documentation Problem solving Algorithms and data structures
Basic shell scripting:

Run programs from a command line
Debug error messages and feedback
Set environment variables
Establish remote connections

Basic statistical knowledge, including:

Populations, samples
Mean, median, mode
Standard error
Variation, standard deviations
Normal distribution

Intermediate differential calculus and linear algebra, including:

Derivatives & Integrals
Series expansions
Matrix operations through eigenvectors and eigenvalues

What software and versions will I need in this program?

You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.6 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages.

Pictures

Become a Deep Reinforcement Learning Expert

Become a Deep Reinforcement Learning Expert Introduction Video

Installation guide

In order to view the lessons of the course in an organized and regular manner, run the index.html file and run the videos through this file.

Udacity – Deep Reinforcement Learning v1.0.0 2020-4