Description

Markov Decision Processes (MDPs) provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. An MDP is defined by its state and action spaces and the one-step dynamics of the environment.

Examples

• 🤖 Robot navigation
• 📦 Inventory management
• 🃏 Poker playing AI

Applications