Techniques & Methods

Reinforcement Learning

Reinforcement learning (RL) trains agents to make sequences of decisions by maximizing cumulative reward signals from the environment. Unlike supervised learning, there are no labeled examples—the agent must explore and discover which actions lead to positive outcomes.

RL underpins game-playing AI (AlphaGo, Atari), robotic control, and the RLHF pipelines used to align language models. Its core challenge is the exploration-exploitation trade-off: balancing trying new actions against exploiting known good ones.

Authority Links

Reinforcement Learning — Wikipedia

Comprehensive overview of RL algorithms and applications.

IBM — Reinforcement Learning

How RL enables agents to learn through trial and reward.

Related Terms

Techniques & Methods

Reinforcement Learning from Human Feedback (RLHF)

Training technique that refines AI models using feedback from human evaluators on output quality.

Techniques & Methods

Proximal Policy Optimization (PPO)

RL algorithm that balances exploration and exploitation by constraining policy update size.

Techniques & Methods

Markov Decision Process

Mathematical framework modeling sequential decision-making in environments with probabilistic outcomes.

Model Components

Reward Models

Models trained to score AI outputs based on human preferences for use in reinforcement learning.

Reinforcement Learning from Human Feedback (RLHF)Regularization