What is Reinforcement Learning and 9 examples of what you can do with it.

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Abstraction: Introductory overview of reinforcement learning with real-world examples

Key points:

RL is a subset of machine learning where an agent learns through consequences of actions in an environment
Differs from supervised learning: no labeled training dataset; learns by trial and error
A series of right decisions reinforces the method, strengthening solution approaches over time
Analogous to human childhood learning: failure and correction build competence
RL can be used to teach robots new tricks and solve sequential decision problems
Behavioral learning model where algorithm provides feedback directing toward the best result

Connections: Reinforcement Learning · Machine Learning