Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
Course Outline
Introduction
- Learning through positive reinforcement
Elements of Reinforcement Learning
Important Terms (Actions, States, Rewards, Policy, Value, Q-Value, etc.)
Overview of Tabular Solutions Methods
Creating a Software Agent
Understanding Value-based, Policy-based, and Model-based Approaches
Working with the Markov Decision Process (MDP)
How Policies Define an Agent's Way of Behaving
Using Monte Carlo Methods
Temporal-Difference Learning
n-step Bootstrapping
Approximate Solution Methods
On-policy Prediction with Approximation
On-policy Control with Approximation
Off-policy Methods with Approximation
Understanding Eligibility Traces
Using Policy Gradient Methods
Summary and Conclusion
Requirements
- Experience with machine learning
- Programming experience
Audience
- Data scientists
Testimonials
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
- Explore
I like examples to explain
- AUO友达光电（苏州）有限公司
information
Amr Alaa - FAB banak Egypt
learning new language.
FAB banak Egypt
subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
helpful and good listener .. interactive
Ahmed El Kholy - FAB banak Egypt
Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course
Mohamed Ghowaiba - FAB banak Egypt
the course is very interesting being the main focus nowdays
mohamed taher - FAB banak Egypt
The discussions to broaden our horizons
FAB banak Egypt
Related Courses
AI in Digital Marketing
7 hours
Genetic Algorithms
28 hours
Intelligent Testing
14 hours
AI and Robotics for Nuclear - Extended
120 hours
AI and Robotics for Nuclear
80 hours
Reinforcement Learning with Java
21 hours
IBM Cloud Pak for Data
14 hours
OptaPlanner in Practice
21 hours