Reinforcement Learning (RL) is an area of AI (Artificial Intelligence) used to build autonomous systems (e.e., an “agent”) that learn by interacting with their environment in order to solve a problem. RL has applications in areas such as robotics, gaming, consumer modeling, healthcare, supply chain management, and more.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
- Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
- Analyze a real-world problem and redefine it as Reinforcement Learning problem.
- Implementing a solution to a real-world problem using Reinforcement Learning.
- Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- A genral understanding of reinforcement learning
- Experience with machine learning
- Java programming experience
- Data scientists
- Solving real-world problems through trial-and-error interactions
Understanding Adaptive Learning Systems and Artificial Intelligence (AI).
How Agents Perceive State
How to Reward an Agent
Case Study: Interacting with Website Visitors
Preparing the Environment for the Agent
Deep Dive into Reinforcement Learning Algorithms
Value-Based Methods vs Policy-Based Methods
Choosing a Reinforcement Learning Model
Using the Q-Learning Model-Free Reinforcement Learning Algorithm
Designing the Agent
Case Study: Smart Assistants
Interfacing the Agent to a Production Environment
Measuring the Results of Agent Actions
Summary and Conclusion