Markov Decision Processes
Machine Learning · Further Education · Machine Learning Applications
Markov Decision Processes (MDPs) provide a framework for modeling decision-making problems. This unit introduces the concepts of MDPs and their application in reinforcement learning, enabling learners to understand and solve complex decision-making tasks.
Zum Üben gibt es 50 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Introduction to Markov Decision Processes
- Components of MDPs: States
- Actions
- Transitions
- Rewards
- Value Functions and Bellman Equations
- Policy Evaluation and Improvement
- Applications of MDPs in Reinforcement Learning
Stichworte
- Markov Decision Processes
- Reinforcement Learning
- Decision-Making Models
- State Transitions
- Reward Functions
- Value Functions
- Policy Evaluation
- Policy Improvement