A Conceptual Introduction to Reinforcement Learning

ONLINE TRAINING
September 29 2020
CLASSROOM TRAINING
November 16 2020
Training Cost
ONLINE TRAINING
545 EUR (VAT ex.) per person
CLASSROOM TRAINING
595 EUR (VAT ex.) per person
Practical informationClass from 9 AM to 5 PM
LanguageEnglish (unless all attendees speak Dutch)
Location

Most of our classroom training courses take placeĀ in Belgium (Edegem) or The Netherlands (Breda). Please click the button with the desiredĀ date to check the exact location of the training.

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Duration

1 day

Course overview

This workshop introduces participants to the concepts and methodologies of Reinforcement Learning (RL). It will enable them to gain an intermediate understanding of what Reinforcement Learning is and in which settings it could be useful. Moreover, the participants will be able to understand and apply recent literature about this topic, qualifying them to further expand the knowledge obtained during the workshop.

Who should attend this training

This course is intended for everyone who is interested in Reinforcement Learning and who wants to gain a better understanding of the topic. It aims to introduce the relevant concepts and methodologies underpinning (deep) Reinforcement Learning through a single, straightforward running example.

Course Objectives

In this workshop, we will introduce the important concepts and methodologies that underpin (deep) Reinforcement Learning, using a single (straightforward) running example.

Prerequisites
  • No previous machine learning knowledge is required, although we definitely recommend some familiarity with Python programming.

  • You need a Google Account so you can start using Colab.

  • This is a BYOD (Bring Your Own Device) training. Please bring your own laptop to the course. Also, make sure to have an up-to-date browser installed.

Course Content
  • What is RL and how is it different from other AI topics?

  • The RL vocabulary: states, actions, rewards and policies.

  • Value functions and the Bellman equations.

  • Solving an RL problem: Dynamic Programming and Q-learning.

  • Scaling up RL: function approximations in deep Reinforcement Learning

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This training in-company?
Upon your request we can organize this training for you.
CONTACT US