Plan Szkolenia

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

Wymagania

  • 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 godzin

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