Model Adaptation · Software component

Reward Shaper

Software componentModel AdaptationModelsarc:RewardShaper

A training-time component that adds intermediate shaping rewards to a sparse reward signal to accelerate learning while preserving the optimal policy.

Responsibility. Densifies sparse rewards with potential-based shaping terms during learning.

Also known as: Reward shaping

Direct neighbourhood (hover for relationship types)

Relationships

reads dependency

Reward Function Specification Ch5.10

sends data to dynamic

Reinforcement Learning Policy Learner Ch5.10

Design guidance

SHOULD use potential-based shaping so the optimal policy under shaped rewards equals that under the original rewards.

Classification

Patterns: Potential-based reward shaping F(s,a,s') = gamma*Phi(s') - Phi(s)
Quality attributes: Performance efficiency (ISO/IEC 25010)
Risks mitigated: Slow learning in sparse-reward environmentsReward hacking of shaped rewards

Sources

Ch5.10: T. Nguyen, "Utility-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.10. ISBN: 9798244538229.