Aimed at independent users, this course in Reinforcement Learning (RL) will take you through some of the practical considerations to make when looking at how Reinforcement Learning can be used as a decision-making tool in your business. This course will take you through practical applications of RL and highlight areas where it can be used in your industry.
Participants will learn about various types of RL algorithms such as Q-learning, REINFORCE Deep Q-learning, and Actor Critic networks while exploring practical application areas from use cases in industry.
You will also complete practical exercises where you will gain some hands-on experience by train a Reinforcement Learning agent in a toy problem. In addition, you will gain an understanding about the importance of hyperparameter optimisation in Reinforcement learning.
In this level course, you will:
- Understand concepts of Reinforcement Learning such as Markov Decision Processes, value and policy-based methods and dynamic programming.
- Gain familiarity to different types of RL algorithms
- Understand areas RL can be used as a decision-making tool.
- Understand the need of hyperparameter tuning in Reinforcement Learning
Pre-requisites:
- Basic calculus and linear algebra
- Basic understanding of machine Learning
- Experience with Python
- Basic understanding of probabilities and expectations
- You may want to look at our Beginner’s Guide to Machine Learning and Data Science course page.
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