Model Training & Adaptation
Proximal policy optimization
A way of teaching AI through rewards while discouraging changes that are too large during learning.
Example
AI learns rewarded behavior while limits discourage very large learning changes.
Why people use it
It helps an agent improve without changing its behavior too sharply at once.
What you'll hear
“Improve the choices without making the learning unstable.”
What this means for you
Check behavior beyond the activities that earned rewards during practice.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does PPO guarantee safe or desirable behavior?
- No. Results depend on the reward, data and training process.
- Does it require human scores for every action?
- No. Feedback can come from a task or another scoring process.
- Can a higher reward hide a worse real-world result?
- Yes. A poorly chosen reward may favor behavior that people do not actually want.