Practical Multi-Armed Bandit Algorithms in Python
Acquire skills to build digital AI agents capable of adaptively making critical business decisions under uncertainties.
4.54 (155 reviews)
906
students
5.5 hours
content
Feb 2022
last update
$69.99
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What you will learn
Understanding and being able to identify Multi-Armed Bandit problems.
Modelling real business problems as MAB and implementing digital AI agents to automate them.
Understanding the challenge of RL regarding the exploration-exploitation dilema.
Practical implementation of the various algorithmic strategies for balancing between exploration and exploitation.
Python implementation of the Epsilon-greedy strategy.
Python implementation of the Softmax Exploration strategy.
Python implementation of the Optimistic Initialization strategy.
Python implementation of the Upper Confidence Bounds (UCB) strategy.
Understand the challenges of RL in terms of the design of reward functions and sample efficiency.
Estimation of action values through incremental sampling.
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udemy ID
4/5/2021
course created date
4/13/2021
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