Advanced · Reinforcement Learning
Partially observable Markov decision process (POMDP)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. An extension of MDPs where the agent cannot directly observe the state, but must infer it from observations.
Technical Definition
An extension of MDPs where the agent cannot directly observe the state, but must infer it from observations.
How it works
A Partially Observable Markov Decision Process (POMDP) is a mathematical framework used in decision theory and AI for modeling decision-making in situations where the system's state is not fully known. The agent must maintain a belief about the current state based on a history of actions and observations.
Related Concepts
- Reinforcement Learning — A paradigm where an agent learns to make decisions by receiving rewards or penalties from its environment through trial and error.
- AI Agent — An AI system that autonomously plans, uses tools, and takes actions to accomplish goals through iterative reasoning.
- Markov decision process (MDP) — A framework for modeling decision-making in random environments where outcomes depend on both controllable actions and chance.