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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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