Cognition · Software component

Value Network Evaluator

Software componentCognitionCognition & Memoryarc:ValueNetworkEvaluator

A state value estimator that evaluates intermediate leaf states directly with a learned value network, replacing or shortening rollouts to terminal states.

Responsibility. Estimates leaf value by neural network prediction of outcome probability.

Also known as: Neural leaf evaluator

Variant of State Value Estimator abstract

When to choose. Choose when training data exists or self-play can generate it and upfront training cost plus per-evaluation inference latency are acceptable.

Direct neighbourhood (hover for relationship types)

Relationships

hosts structural

Value Network Ch5.5

alternative to variability

Rollout Simulator Ch5.5

Quantitative guidance

As stated by the sources; verify before use.

Initial policy and value network training takes weeks of GPU time (Ch5.5).

Classification

Patterns: Rollout-free evaluation (AlphaZero)Value network plus short rollout (AlphaGo)
Quality attributes: Performance efficiency (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Noisy random-rollout value estimates

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.