Review:
Markov Decision Processes (mdp)
overall review score: 4.5
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score is between 0 and 5
Markov Decision Processes (MDP) are a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
Key Features
- States
- Actions
- Rewards
- Transition probabilities
Pros
- Provides a formal and rigorous framework for decision-making under uncertainty
- Used in a wide range of applications including robotics, artificial intelligence, economics, and healthcare
Cons
- Can be computationally expensive for large state spaces
- Requires careful parameter tuning for optimal performance