Course Outline

Introduction

  • Learning through positive reinforcement

Elements of Reinforcement Learning

Important Terms (Actions, States, Rewards, Policy, Value, Q-Value, etc.)

Overview of Tabular Solutions Methods

Creating a Software Agent

Understanding Value-based, Policy-based, and Model-based Approaches

Working with the Markov Decision Process (MDP)

How Policies Define an Agent's Way of Behaving

Using Monte Carlo Methods

Temporal-Difference Learning

n-step Bootstrapping

Approximate Solution Methods

On-policy Prediction with Approximation

On-policy Control with Approximation

Off-policy Methods with Approximation

Understanding Eligibility Traces

Using Policy Gradient Methods

Summary and Conclusion

Requirements

  • Experience with machine learning
  • Programming experience

Audience

  • Data scientists
 21 Hours

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