Model Training & Adaptation
Policy gradient
Teaching AI to adjust its action choices toward ones expected to earn more reward.
Example
Training makes rewarded actions more likely to be chosen.
Why people use it
It teaches an agent by changing how likely it is to choose particular actions.
What you'll hear
“Make rewarded choices more likely in future attempts.”
What this means for you
Check unintended effects as well as whether the reward increases.
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
- Can learning favor a shortcut people did not want?
- Yes. If the reward is poorly chosen, a shortcut may score well without completing the intended task.
- Does it always choose the best-looking action immediately?
- No. Learning can use varied action choices to gather useful experience.
- Can delayed rewards make learning harder?
- Yes. It can be difficult to identify which earlier choices contributed to a later result.