Markov Decision Process
Noun · AI & Machine Learning
Definitions
Markov Decision Process is an evaluation concept used to measure model quality, robustness, or efficiency. It is commonly used for comparing systems before release or model promotion, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to representative tasks, metric choice, and leakage, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.
In plain English: Markov Decision Process is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.
Example: "We evaluated Markov Decision Process in the new model pipeline because the baseline was plateauing; once it was wired into training and evaluation, quality improved enough to justify rolling it into the next release."
Related Terms
- Actor-Critic
- Deep Q-Network
- Deterministic Policy
- Inverse Reinforcement Learning
- Offline Reinforcement Learning
- Policy Gradient
- PPO
- Proximal Policy Optimization
- Q-Learning
- Reinforcement Learning Detail
- Reinforcement Learning from AI Feedback
- Reward Function
- Reward Hacking
- Reward Model
- Support Vector Machine
- MCP Server
- PPO Algorithm