Core AI · Intermediate · 20-40 hours
Reinforcement Learning
Learning behavior by maximizing expected reward through interaction.
Direct answer
What is Reinforcement Learning?
Learning behavior by maximizing expected reward through interaction.
Definition and scope
Learning behavior by maximizing expected reward through interaction.
An agent observes state, takes actions, receives rewards, and updates a value function or policy.
Why it matters
RL can discover strategies that are difficult to specify as demonstrations or rules.
How it works
An agent observes state, takes actions, receives rewards, and updates a value function or policy.
Beginner learning path
Learn Markov decision processes, value functions, policy gradients, exploration, and offline RL.
Recommended next topics
Primary sources
Key papers
Dreamer: Reinforcement Learning with Latent Imagination
Dreamer learns long-horizon behavior by propagating value gradients through imagined latent trajectories.
DreamerV2
DreamerV2 extends latent imagination with discrete representations and reaches human-level Atari performance.
DreamerV3: Mastering Diverse Domains through World Models
DreamerV3 uses robust normalization and objectives to learn across more than 150 tasks with one configuration.
Research ecosystem
Organizations working in this area
Organization
Google DeepMind
World models, robot learning, VLA systems, embodied reasoning
View profile →Organization
OpenAI
General-purpose AI, reinforcement learning, robotics research
View profile →Common questions
Frequently asked questions
What is Reinforcement Learning?
Learning behavior by maximizing expected reward through interaction.
Why does Reinforcement Learning matter for Physical AI?
RL can discover strategies that are difficult to specify as demonstrations or rules.
How should a beginner learn Reinforcement Learning?
Learn Markov decision processes, value functions, policy gradients, exploration, and offline RL.