Duration
21 hours (usually 3 days including breaks)
Requirements
- A genral understanding of reinforcement learning
- Experience with machine learning
- Java programming experience
Audience
- Data scientists
Overview
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.
Course Outline
Introduction
- 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
Troubleshooting
Summary and Conclusion