Markov Decision Processes
A mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker.
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
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