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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
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses