Methoden vergleichen

	Multi-objective Markov Model	Stochastic Dynamic Programming
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2006	1957
Urheber	Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.)	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
Typ	Stochastic sequential decision model with multiple objectives	Sequential optimization under uncertainty
Wegweisende Quelle	Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113.	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
Aliasnamen	MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model	SDP, Markov Decision Process, MDP, Stochastic DP
Verwandt	5	6
Zusammenfassung	A Multi-objective Markov Model (MOMDP) extends classical Markov Decision Processes to settings where an agent must optimize several reward signals simultaneously. Instead of a single optimal policy, the model produces a Pareto-optimal set of policies, enabling decision-makers to navigate trade-offs between competing goals such as cost, risk, and throughput over time.	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.