Rlhf: Reinforcement Learning From Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that combines reinforcement learning with human feedback to train AI agents, particularly in tasks where defining a reward function is challenging, such as human preference in natural language processing.
Areas of application
- Natural Language Processing
- Computer Vision
- Robotics
- Gaming
Example
For instance, an RLHF system could be trained on a text classification task, where the agent learns to classify news articles as positive or negative based on human feedback received through user interactions with the system.