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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 is the technique behind AI's most spectacular achievements in games, AlphaGo beating the world Go champion, agents dominating StarCraft and Dota, and it finds applications in robotics, systems optimization and self-driving cars.

In language models, one of its variants is central: RLHF (Reinforcement Learning from Human Feedback), the process by which models like ChatGPT and Claude are refined using human preferences as the reward signal. It's what makes them helpful, safe and able to follow instructions.

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

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