Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Προγραμματισμός Δυναμικής Βάσει Πρακτόρων× | Στοχαστικός Δυναμικός Προγραμματισμός× | |
|---|---|---|
| Πεδίο | Προσομοίωση | Προσομοίωση |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1957 (DP); 1990s onward (ABM integration) | 1957 |
| Δημιουργός≠ | Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Τύπος≠ | Hybrid simulation-optimization | Sequential optimization under uncertainty |
| Θεμελιώδης πηγή | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Εναλλακτικές ονομασίες | ABDP, Agent-based DP, Multi-agent dynamic programming, ABM-DP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Συναφείς≠ | 5 | 6 |
| Σύνοψη≠ | Agent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, multi-stage decision problems using backward induction or value-function iteration. The result is a population of optimizing agents whose interactions generate emergent system-level behavior. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|