Model Adaptation · Software component
Policy Learner
Software componentModel AdaptationModelsVariation point (abstract)arc:PolicyLearner
An abstract model-adaptation component that updates a learned policy model from interaction experience or expert demonstrations.
Responsibility. Updates the learned policy model from experience or demonstrations.
Also known as: Learning algorithm, Policy trainer
Variants
| Variant | When to choose |
|---|---|
| Imitation Learner abstract | Choose when expert demonstrations are available and autonomous exploration is too slow, costly, or unsafe. |
| Multi-Agent Policy Learner abstract | — |
| Reinforcement Learning Policy Learner | Choose when a reward signal is available and the agent can safely explore (e.g., in simulation), with ample data and compute. |
Relationships
trains lifecycle
- Learned Decision Policy abstract Ch5.12
Design guidance
- SHOULD train offline in simulation or controlled settings when real-world failures are costly, then adapt gradually during deployment.
Classification
- Patterns
- Offline trainingOnline adaptationTransfer learning from simulation to real world
Sources
- Ch5.12: T. Nguyen, "Learning-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.12. ISBN: 9798244538229.