Reinforcement Learning, from an Everyday Analogy

The dog-training analogy for reinforcement learning, and the four pieces every RL problem is built from: state, action, reward and policy, illustrated with an adaptive honeypot example.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Explain reinforcement learning using the dog-training analogy, without any formal notation
Define state, action, reward and policy for a security example of your own choosing
Explain why reinforcement learning differs in kind, not just method, from the supervised and unsupervised learning in Units 2 and 3

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

Reinforcement Learning, from an Everyday Analogy

You do not hand a dog a labelled dataset of good sit and bad sit. You let it try something and reward what works. That is reinforcement learning, and it never needed a single labelled example.

Quadrant 3 · Web Resources