Model Adaptation · Software component
Inverse RL Reward Learner
Software componentModel AdaptationModelsarc:InverseRLRewardLearner
An imitation learner that infers the reward function expert demonstrations optimize, then obtains a policy by reinforcement learning on that inferred reward.
Responsibility. Infers the expert's reward function from demonstrations.
Also known as: Inverse reinforcement learning, Maximum entropy IRL
Variant of Imitation Learner abstract
When to choose. Choose when demonstrations come from multiple experts with different strategies, deployment differs qualitatively from demonstrations, or the reward is needed for explanation or policy evaluation.
Relationships
invokes dependency
reads dependency
trains lifecycle
alternative to variability
Design guidance
- SHOULD treat the recovered reward as one hypothesis among many consistent with the demonstrations (reward identifiability problem).
Quantitative guidance
As stated by the sources; verify before use.
- Wheelchair example: inferred weights 0.1 distance, 0.3 collision avoidance, 0.6 smoothness (Ch5.12).
Classification
- Patterns
- Maximum entropy inverse reinforcement learning
- Risks mitigated
- Incoherent averaging of inconsistent multi-expert demonstrationsPoor generalization of memorized state-action mappings
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.