Markov Decision Processes
What is Markov Decision Processes?
Markov Decision Processes (MDPs) are essential in the field of artificial intelligence for modeling environments where an agent makes decisions to achieve a goal. An MDP is defined by a set of states, a set of actions, transition probabilities that describe the likelihood of moving from one state to another after taking an action, and a reward function that provides feedback on the desirability of each state. The goal is to find a policy, or strategy, that maximizes the cumulative reward over time. MDPs are widely used because they provide a structured way to handle uncertainty and optimize decision-making, which is crucial for developing intelligent systems in various applications such as robotics, finance, and healthcare. By understanding the dynamics of the environment and systematically planning actions, MDPs help create more efficient and effective AI solutions.
A mathematical framework used in artificial intelligence to model decision-making problems where outcomes are partly random and partly under the control of a decision-maker.
Examples
- Robotics: In robotic navigation, MDPs are used to determine the best path for a robot to take in an uncertain environment, like navigating through a cluttered room to reach a specific destination.
- Healthcare: MDPs help in personalized treatment planning for chronic diseases by optimizing the sequence of treatments over time to maximize patient outcomes and minimize side effects.
Additional Information
- MDPs assume the Markov property, meaning the future state depends only on the current state and action, not on the history of past states.
- Solving an MDP typically involves dynamic programming techniques like Value Iteration or Policy Iteration.