Reinforcement Learning (RL) is a machine learning technique where an agent learns to make decisions by interacting with its environment. Through this process, the agent takes actions, receives feedback as rewards or penalties, and refines its strategy to maximize long-term rewards. This trial-and-error approach allows for continual improvement in performance without explicit programming instructions.
Applications:
- Robotics: Used to train robots, such as navigating a maze by earning rewards for reaching the exit and avoiding penalties for hitting walls.
- Web Design: Optimizes user experiences by dynamically adjusting website layouts or content based on user interactions to boost engagement and conversions.
Benefits:
- Adaptive Learning: Continuously improves strategies over time.
- Autonomy: Operates without requiring direct programming for every scenario.
For additional information, explore related concepts like “Machine Learning” and “Artificial Intelligence in User Experience.”
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