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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
Delivery Options
Private Group Training
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- 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 £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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