Introduction to RL
Dynamic Programming
The RL Framework
The RL Framework: The Solution
Temporal - Difference Methods
RL In Continuous Spaces
Solve openai Gym’s Taxi - V2 Task
Monte Carlo Methods
Online
4 Months
Quick facts
particular | details | |||
---|---|---|---|---|
Collaborators
Unity Technologies,
+1 more
|
Medium of instructions
English
|
Mode of learning
Self study, Virtual Classroom
|
Mode of Delivery
Video and Text Based
|
Learning efforts
10-15 Hours Per Week
|
Course overview
This course by Udacity has been specially crafted for candidates seeking to improvise their machine learning and deep learning skills. Pioneering machine learning algorithms have been focused on along with furnishing course-takers with hands-on coding experience. These exercises are challenging to prepare the candidates for the best and the worst. They are also unrestricted and cover a variety of concepts.
This Deep Reinforcement Learning course online certification deals with unique concepts which make this course stand out. Using a combination of Python and deep learning libraries, the candidates can implement their learnings for good. The projects submitted by students shall form their portfolio, entitling them to lucrative jobs in the field. The course basically explores the budding interest and trends in the deep learning arena so that candidates can strike a niche in their career.
Experienced mentors, scientifically-crafted syllabus, additional resources for improvisation and many more features make this course an ideal fit for exploring the spell-bounding innovations in Artificial Intelligence. The course serves as the perfect foundation for learning the mechanisms concerning gaming, robotics, and financial trading.
The highlights
An expansive syllabus spreading over four months
Ten hours of learning experience each week
Co-created course content by Unity, Nvidia, and Deep Learning Institute
Simultaneous career counselling
Certification by Udacity
Program offerings
- Real-world projects
- Technical mentor support
- Online flexible learning
- Classroom learning
- Project feedback
Course and certificate fees
- You can pay the Deep Reinforcement Learning Expert fee upfront or pay as you go
- The upfront payment will provide you with 4 months of access
Deep Reinforcement Learning Expert Fee Structure
Particular | Amount in INR |
4-month access | Rs. 77,676 |
Pay as you go | Rs. 22,849/month |
certificate availability
certificate providing authority
Eligibility criteria
Work Experience
Candidates taking this course must have an intermediate level experience with advanced Python language and object-oriented programming. They should be able to read and decipher the codes written by others.
Apart from this, course-takers must also possess an intermediate-level statistics background with a satisfactory familiarity with probability. They should have a grasp over machine learning techniques with an experience of propagation and acknowledgement of neural network architectures.
Lastly, they must also be aware of how deep learning frameworks like TensorFlow or PyTorch work.
Education
Udacity recommends students to pursue the Deep Learning Nanodegree programme prior to pursuing the present programme. Candidates must be professionally fluent in oral and written English.
Other educational requirements include intermediate knowledge of Python and its relevant concepts, basic shell scripting, primary knowledge of statistics, and intermediate differential calculus and linear algebra.
Certification Qualifying Details
The programme Deep Reinforcement Learning course comprises curriculum, content and three projects. These projects need to be completed and submitted within four months of time duration. These projects will then be checked and evaluated by the reviewer network of Udacity. Failing to clear these projects will not provide you with the certification. It is essential to pass in these projects to achieve certification of completion.
What you will learn
A thorough study of Deep Reinforcement Learning programme would facilitate the candidates to-
Acquire expertise over deep learning and reinforcement learning.
Be vested with the skills required to understand the current trends in deep reinforcement learning.
Build and implement relevant algorithms.
Deploy the takeaways to train agents to perform simple and complex tasks.
Learn the mode of application of reinforcement learning methods to multi-interactive applications.
Who it is for
The programme on Deep Reinforcement Learning is particularly helpful for learning cutting-edge algorithms instrumental in various overlapping industries. This would cater to the needs of-
Video game developers and Robotics engineers who wish to implement AI in gaming and robotic models.
Deep learning and machine learning enthusiasts who want to build a portfolio for jobs and enhance their knowledge in their respective fields.
Individuals preparing themselves for the role of engineers who are sought after in the industry for their skills on deep reinforcement learning.
Admission details
There are no admission criteria for the Deep Reinforcement Learning course and any candidate can avail of the course irrespective of his background provided he fulfils the formalities. Udacity offers the option to pay the course fee in whole or in instalments payable each month.
The following steps must be carried out for registering in the programme-
Step 1: Go to the homepage of the Deep Reinforcement Learning course.
Step 2: Click on the ‘Enroll Now’ option.
Step 3: Select a payment plan depending upon your preference
Step 4: Depending on whether you are enrolling for the first time or whether you are a regular course-taker, you may choose between ‘Quick Checkout’ and ‘Returning Student’ respectively.
Step 5: Clicking on Quick Checkout will require you to sign up via your Google or Facebook account. In the case of the latter option, you must sign up from your Udacity account.
Step 6: The webpage shall now display the e-bill specifying the base price, bundle discount, and the total amount chargeable.
Step 7: You may enter the discount coupon code if you have one. Else, click on ‘Continue With Checkout’ to finalise your plan.
Step 8: You are then supposed to put in your billing details
Step 9: You will be provided immediate access to the classroom after the payment is successful.
The syllabus
Foundation of Reinforcement Learning
Value Based Methods
Deep Q-Learning
Deep Learning in PyTorch
Deep RL for Robotics
Policy Based Methods
Introduction to Policy-Based Methods
Actor-Critic Methods
Improving Policy Gradient Methods
Deep RL for Financial Trading
Multi-Agent Reinforcement Learning
Case Study: Alphazera
Introduction MultiAgent RL
Scholarship Details
Exhaustive details on scholarship programmes by Udacity are available on the course page. After visiting the link, candidates must sign up for the programme by entering relevant information under the “Notify Me” section. Post-sign-up, they will be eligible for scholarships and will also be entitled to receive notifications regarding present and future scholarships.
The webpage features a “Learn More” tab which would redirect a prospective candidate to the webpage concerning scholarship programmes suited to his/her needs.
How it helps
Deep Reinforcement Learning has immense industrial significance given the fact that software giants like Apple, Facebook, and Google are investing in it. Engineers having a strong hand over the field can expect handsome job opportunities and packages. This programme prepares the candidates for the outside world in this aspect. Building a robust portfolio of the projects undertaken by the students is one step in that direction.
The highly interactive mode of learning through personal career coaching, mentor support, knowledge portal and other features makes the course engaging and understandable. Pedagogy also aims at preparing the candidates for interviews and preparing their resume to perfection.
The lessons have been crafted so that candidates become able enough to write their own implementation of classical solution methods and apply the architectural framework to functioning tasks. Creation of an agent which navigates a virtual world from sensory data is also a major benefit of the course. From studying the theory behind evolutionary algorithms and policy-gradient methods to deploying it in designing algorithms for manipulating a robotic arm, this programme teaches it all.
Instructors
Mr Miguel Morales
Content Developer
Freelancer
Other Masters
Mr Juan Delgado
Content Developer
Udacity
Other Masters, Ph.D
Ms Chhavi Yadav
Content Developer
Freelancer
Other Bachelors
Ms Dana Sheahen
Content Developer
Freelancer
M.E /M.Tech.
Ms Cezanne Camacho
Instructor
Freelancer
M.S, Other Masters
Mr Luis Serrano
Instructor
Freelancer
Ph.D
Mr Mat Leonard
Instructor
Freelancer
Ph.D
Mr Arpan Chakraborty
Instructor
Georgia Tech
Ph.D
Ms Alexis Cook
Instructor
Freelancer
Other Masters
FAQs
Such a candidate can pursue a few nanodegree programmes offered by Udacity including-
Machine Learning Engineer Nanodegree program
Artificial Intelligence Programming with Python Nanodegree program
Deep Learning Nanodegree program
Intro to Machine Learning
Course-takers require a computer with a 64-bit OS (could be the most modern versions of Windows, OS X, and Linux). The computer should have a RAM of at least 8GB along with administrator account permissions for installing programs like Anaconda with Python 3.6 and other supporting packages.
Udacity provides a student with the rubric used to grade a project. A completed and submitted project is checked against the rubric and needs to be submitted by the candidate on the site. A reviewer will use the same rubric to review a submission.
No. Udacity has zero-tolerance for plagiarized work, which shall be outright rejected on submission. Another disciplinary action including expulsion from the programme or Udacity without a refund and the revocation of the candidate’s graduation credential may also be taken.
You can download the programme content by navigating to the ‘Lesson Concept’ page where all the videos are displayed, opening the ‘Resources’ tab in the left navigation menu and clicking on the link against each video to download it.