AI Dictionary › AI Fundamentals

Reinforcement Learning

Reinforcement learning is the paradigm where an AI agent learns by trial and error, receiving rewards for good actions and penalties for bad ones. It is not shown correct examples: it discovers the best strategy by maximizing cumulative reward.

Definition

It powers AI's most spectacular achievements in games (AlphaGo) and applies to robotics and autonomous systems. In language models, its variant RLHF, using human preferences as the reward signal, is what makes ChatGPT and Claude helpful and safe.

Related terms

• RLHF
• Machine Learning
• Supervised Learning
• AI Agent
• Deep Learning

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

Put it into practice

• Real scenario: apply this concept with an instant AI verdict.
• Daily gym: short drills to train dictionary concepts.
• Playoff game: challenge other players, elimination-style.

📱 Download the Android app (beta) iOS coming soon

Say what you mean. Get what you need.

Grace Certified, the AI coach that trains and certifies your prompt engineering, by Agora Intelligence.