4.5 Reinforcement Learning

Reinforcement learning = learning by trial and error, with rewards for good moves and penalties for bad ones.

Chapter 4: Introduction to Machine Learning (ML)

This one's like training a pet, or how you got good at a video game. An agent takes an action in an environment, gets a reward (or penalty), and adjusts to get more rewards next time.

There are no labelled answers - just feedback. Over millions of attempts the agent figures out a strategy. DeepMind's AlphaGo used reinforcement learning (plus other techniques) to beat world champion Lee Sedol at Go in 2016.

It's also used for robots learning to walk, game AI, and fine-tuning chatbots with human feedback (called RLHF) so their answers are more helpful.

Key takeaways

Learning by trial and error with rewards

No labels, just feedback

Used in games, robotics and RLHF for chatbots

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