Cognition · Software component
MDP Policy Solver
Software componentCognitionCognition & Memoryarc:MDPPolicySolver
A decision engine that models a problem as a Markov decision process and derives a policy maximizing expected discounted sum of future rewards.
Responsibility. Computes action policies maximizing long-term discounted utility over state transitions.
Also known as: Sequential utility-based decision maker, Markov Decision Process planner
Variant of Decision Engine abstract
When to choose. Choose for sequential decisions where current actions affect future options and the state is (or has been augmented to be) Markovian.
Relationships
is configured by structural
invokes dependency
receives data from dynamic
alternative to variability
Design guidance
- MUST validate the Markov property before applying an MDP rather than assuming it holds.
- SHOULD choose the discount factor from domain characteristics: high (near 1) for long planning horizons, lower for uncertain futures with high disruption probability.
- SHOULD distinguish immediate rewards from long-term utility (expected sum of discounted future rewards).
Classification
- Patterns
- Markov Decision ProcessDiscounted expected returnSequential expected utility over treatment/decision pathways
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Myopic optimization of immediate 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.