Course Outline

Introduction

Reinforcement Learning Basics

Basic Reinforcement Learning Techniques

Introduction to BURLAP

Convergence of Value and Policy Iteration

Reward Shaping

Exploration

Generalization

Partially Observable MDPs

Options

Logistics

TD Lambda

Policy Gradients

Deep Q-Learning

Topics in Game Theory

Summary and Next Steps

Requirements

  • Proficiency in Python
  • An understanding of college Calculus and Linear Algebra
  • Basic understanding of Probability and Statistics
  • Experience creating machine learning models in Python and Numpy

Audience

  • Developers
  • Data Scientists
  21 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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