Reinforcement Learning, from first principles to production agents
Reinforcement Learning is the branch of machine learning where an agent learns by acting, observing, and being rewarded — no labeled dataset required. It underpins everything from game-playing agents and robotics to the RLHF pipelines that align today's large language models. This hub is your starting point: the core loop, the vocabulary, why it matters right now, and where to go deeper.
The agent–environment–reward loop
Every RL system, from a tabular Q-learning toy problem to a multi-billion-parameter RLHF policy, runs on the same closed loop.
Core concepts at a glance
A quick-reference glossary before diving into the deep-dive guide or the algorithms breakdown.
State & Observation
The information available to the agent at a given timestep — fully or partially describing the environment.
Action Space
The set of moves available to the agent: discrete (e.g. board-game moves) or continuous (e.g. robot joint torques).
Reward Signal
A scalar feedback value the agent tries to maximize over time — the only supervision RL requires.
Policy (π)
The agent's strategy: a mapping from states to actions, either deterministic or a probability distribution.
Value Function
An estimate of expected future reward from a state (or state-action pair), used to guide better decisions.
Exploration vs. Exploitation
The core trade-off: try new actions to discover better strategies, or exploit the best known one so far.
Why reinforcement learning matters in 2026
RL has moved well beyond Atari and board games — it's now core infrastructure for aligning and improving frontier AI systems.
LLM Alignment (RLHF / RLAIF)
Reinforcement Learning from Human (or AI) Feedback is the standard post-training step that shapes today's chat and reasoning models toward helpful, safe behavior.
Robotics & Embodied AI
Policy-gradient and actor-critic methods drive locomotion, manipulation, and sim-to-real transfer for physical and humanoid robots.
Agentic AI Systems
Multi-step, tool-using AI agents increasingly rely on RL-style reward shaping and self-play to improve planning and long-horizon task success.
Sequential Decision-Making
From trading strategies to resource scheduling and recommendation systems, RL formalizes decisions made over time under uncertainty.