Fondamentaux

Reinforcement Learning (RL)

Définition

A machine learning paradigm where an agent learns by interacting with an environment and receiving reward or penalty signals. RL is the foundation of game-playing systems like AlphaGo and increasingly powers robotics, logistics optimisation, and dynamic pricing.

Termes Connexes

Reinforcement Learning from Human Feedback (RLHF)

A training technique that refines language model behaviour by learning from human preferences rather than fixed labels. RLHF is a primary method used to align LLMs like ChatGPT and Claude with desired values and reduce harmful outputs.

Autonomous Systems

Systems capable of performing complex tasks in real-world environments without human intervention for each action. In regulated sectors such as transport, healthcare, and finance, autonomous AI systems typically fall under the EU AI Act's high-risk classification.

Embodied AI

AI systems situated in a physical body or robot that perceive the world through sensors and act on it through actuators. Embodied AI bridges software intelligence and the physical world, making it central to robotics, autonomous vehicles, and smart manufacturing.

Services Connexes

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