Cognition · Software component
Learned-Policy Decision Engine
Software componentCognitionCognition & Memoryarc:LearnedPolicyDecisionEngine
A decision engine that selects actions by querying a policy or action-value model learned from experience or demonstrations, balancing exploration of new actions against exploitation of known good ones.
Responsibility. Maps the observed state to an action using a learned policy model.
Also known as: Learning-based decision maker, RL agent policy executor, Operational learning-based controller, Learned policy
Variant of Decision Engine abstract
When to choose. Choose when the strategy space is too large to specify manually, the environment is dynamic or partially unknown, and sufficient training data and computation are available.
Relationships
hosts structural
- Learned Decision Policy abstract Ch5.12
reads dependency
- Execution Plan abstract Ch5.13
writes dependency
is routed to by dynamic
sends data to dynamic
- Decision Fusion Aggregator abstract Ch5.13
triggers dynamic
is guarded by control
alternative to variability
Design guidance
- SHOULD explore aggressively when knowledge is sparse, exploit increasingly as estimates improve, and retain some exploration indefinitely to detect environmental change.
- SHOULD NOT be used as the sole decision mechanism where regulation requires human-interpretable explanations or safety requires formal verification.
- SHOULD bootstrap from rule-based or utility-based priors, expert demonstrations, or simulation-trained policies rather than learning from scratch in the real world.
- SHOULD be paired with rule-based safety reflexes that act regardless of learned policy output in safety-critical deployments.
Quantitative guidance
As stated by the sources; verify before use.
- Epsilon decays as max(0.01, epsilon x 0.995) per episode: ~1.0 early, ~0.1 by episode 500, 0.01 by episode 1000 (Ch5.12 grid world).
Classification
- Patterns
- Epsilon-greedy exploration with decayOptimistic initializationBoltzmann (softmax) explorationUpper confidence bound (UCB) explorationDeterministic vs. stochastic policyDecentralized execution of centrally trained policiesLearned inter-agent communicationOnline adaptation during deployment
- Quality attributes
- Flexibility (ISO/IEC 25010)Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Brittleness of hand-specified rules in unanticipated situationsOutdated decision logic in evolving environments (e.g., new fraud patterns)
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.
- 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.