100 Best Markov Decision Process Videos


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 used in a variety of fields, including economics, computer science, and engineering, to model and analyze complex decision-making situations.

In an MDP, a decision maker must choose a sequence of actions in order to achieve a desired outcome. Each action has a associated cost or reward, and the decision maker must choose actions that will maximize the expected rewards while minimizing the costs. MDPs are used to model these types of decision-making situations by representing the possible states of the system, the actions that can be taken, and the probabilities of transitioning between different states. This allows the decision maker to evaluate different courses of action and choose the one that is most likely to lead to the desired outcome.



See also:

HTK (Hidden Markov Model Toolkit) & Dialog SystemsMCMC (Markov Chain Monte Carlo) & Dialog SystemsPOMDP (Partially Observable Markov Decision Process) & Dialog Systems

[163x Nov 2017]