Reinforcement Learning Primer

Reinforcement Learning Primer is a structured learning app for understanding the main ideas behind reinforcement learning and decision-making systems.

What you will study:
- agents, environments, rewards, and returns
- value functions, Bellman intuition, and policy learning
- exploration and exploitation tradeoffs
- policy gradients, actor-critic methods, and model-based ideas
- practical stability concerns in RL training
- introductory connections between RL concepts and modern AI study topics

Learning design:
- chapter-based lessons for self-study
- short explanations followed by quizzes and recap points
- local progress tracking on the device
- no account required to begin

Who it is for:
- learners building reinforcement learning fundamentals
- developers moving from model basics into sequential decision topics
- students who want a guided RL overview before deeper study