Reinforcement Learning from Human Feedback
Machine Learning
Learning by interacting with an environment to maximize rewards.
Detailed Explanation
Reinforcement Learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. It receives feedback in the form of rewards or penalties based on its actions, aiming to maximize cumulative rewards over time. This approach is commonly used in robotics, game playing, and complex decision-making tasks.
Use Cases
- •Use case: Training a robot to navigate and complete tasks efficiently by learning from rewards received after each successful or failed action.
Related Terms
Other terms in the Machine Learning category