Model Adaptation · Software component
Reinforcement Learning Policy Learner
Software componentModel AdaptationModelsarc:ReinforcementPolicyLearner
A learning component that acquires a decision policy by trial-and-error interaction, updating it from received reward signals.
Responsibility. Learns a decision policy from reward feedback through trial and error.
Also known as: Reinforcement learning agent, RL trainer, Deep RL learner
Variant of Policy Learner abstract
When to choose. Choose when a reward signal is available and the agent can safely explore (e.g., in simulation), with ample data and compute.
Relationships
is configured by structural
invokes dependency
is invoked by dependency
reads dependency
writes dependency
receives data from dynamic
is monitored by assurance
trains lifecycle
alternative to variability
- Imitation Learner abstract Ch5.12
Design guidance
- SHOULD sample decorrelated mini-batches from an experience replay buffer and compute targets with a periodically synchronized frozen target network when training neural value functions.
- SHOULD constrain policy updates to a trust region (e.g., PPO clipping) to trade speed for stability.
- SHOULD use policy-gradient or actor-critic methods for continuous action spaces.
Quantitative guidance
As stated by the sources; verify before use.
- Grid world: 1,000 episodes to master 4x4; average reward ~-50 (episodes 1-100), ~-20 (100-300), ~+60 (300-600), >+85 (600-1000) (Ch5.12).
- Target network syncs every 10,000 training steps (Ch5.12).
Classification
- Patterns
- Reinforcement learningQ-learningTemporal-difference learningValue iteration / policy iteration (known dynamics)REINFORCE policy gradientActor-criticProximal Policy Optimization (clipped ratio)Deep Q-Network with target networkDouble DQNRainbow DQNDDPGTwin Delayed DDPGSoft Actor-Critic (entropy regularization)Reward shapingContinuous retraining on recent data
- Quality attributes
- Flexibility (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Training divergence from correlated samples and moving targetsCatastrophic policy updates
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.
- 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.
- Ch5.13: T. Nguyen, "Hybrid Decision Systems Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.13. ISBN: 9798244538229.