Technique

Reinforcement Learning (RL)

Reinforcement learning trains AI agents through trial and error with a reward system — the agent learns optimal strategies by receiving positive or negative feedback on its actions.

What Is Reinforcement Learning?

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions. Unlike supervised learning (which needs labeled examples), RL discovers optimal strategies through exploration. RLHF (Reinforcement Learning from Human Feedback) is the technique used to align LLMs with human preferences.

Where Is Reinforcement Learning Used in Business?

RL powers dynamic pricing optimization, supply chain routing, recommendation systems, ad bidding strategies, and robotic process control. It's particularly valuable for problems where the optimal strategy isn't obvious and must be discovered through experimentation.

reinforcement learningRLRLHFreward learning

Related Terms

Machine Learning (ML)

Machine learning is a subset of AI where systems learn patterns from data and improve performance without being explicitly programmed for each task.

AI Alignment

AI alignment is the research field focused on ensuring AI systems behave in accordance with human intentions, values, and goals — preventing unintended or harmful outcomes.

Agentic AI

Agentic AI refers to autonomous AI systems that can plan, execute, and learn from multi-step tasks without human intervention at each step — managing complete business workflows independently.

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