Reinforcement learning (RL)

Reinforcement learning is training by trial and error. An agent (a program that acts) tries actions in an environment, receives a numerical reward when things go well, and gradually learns which actions lead to the most reward over time. Deep reinforcement learning uses a neural network to decide which action to take.

Why it comes up

DeepMind under Hassabis, with David Silver leading the work, used reinforcement learning to build AlphaGo and AlphaZero; Silver argues that reward maximization is enough to produce general intelligence, and labs now use it to train language models to reason.

People and milestones

AlphaGo defeats Lee Sedol, 2016 OpenAI o1 and test-time reasoning, 2024 DeepSeek-R1, 2025

David Silver

Demis Hassabis

Where it comes up on Frontier Minds

Andrew Ng
Dario Amodei

Sources

Reinforcement Learning, An Introduction, 2nd edition (Sutton and Barto, 2018)
Mastering the game of Go with deep neural networks and tree search (Silver and others, Nature, 2016)