Cognition · Model asset
Policy Network
Model assetCognitionCognition & Memoryarc:DeepPolicyNetwork
Neural network weights trained on expert demonstrations to predict action probabilities for a state, used as search priors and rollout guidance.
Responsibility. Predicts expert-like action probabilities for a state.
Also known as: Manipulation policy network, RL policy, Deep Q-Network, Actor network, Policy network, Deep RL policy, Policy Network, Control Policy Model
Variant of Learned Decision Policy abstract
When to choose. Choose when states are high-dimensional (images, sensors) or actions are continuous, so tables cannot be stored or visited.
Relationships
deployed on structural
is trained by lifecycle
alternative to variability
Quantitative guidance
As stated by the sources; verify before use.
- AlphaGo's policy network was trained on millions of expert games (Ch5.5).
- Roughly one million parameters can capture Q-value patterns across trillions of states (Ch5.12).
- DQN input is a stack of the four most recent game frames (Ch5.12).
- AV operational policy outputs steering -30 to +30 degrees, throttle 0-100%, brake 0-100% (Ch5.13).
Classification
- Patterns
- Function approximationConvolutional feature extraction from pixelsFrame stackingDueling architectureContinuous action output
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Performance efficiency (ISO/IEC 25010)
- Risks mitigated
- Curse of dimensionality in tabular methods
Sources
- Ch5.5: T. Nguyen, "Monte Carlo Tree Search Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.5. ISBN: 9798244538229.
- Ch5.7: T. Nguyen, "Episodic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.7. 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.