Model Training & Adaptation
Deep reinforcement learning
AI learning through rewards and penalties using a large network of connected calculations.
Example
An AI learns actions in a practice environment by earning rewards.
Why people use it
It lets AI learn complex action choices through repeated attempts and feedback.
What you'll hear
“The agent improves by trying things and earning rewards.”
What this means for you
Check its behavior in the conditions where it will actually be used.
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 it learn from failed attempts?
- Yes. Feedback about an unsuccessful action can help it choose differently later, if that feedback is useful.
- Does success in a simulation guarantee success outside it?
- No. Real conditions can differ from the simplified environment used for practice.
- Can the reward encourage an unwanted shortcut?
- Yes. The system may learn an easy way to earn points that misses the intended goal.