Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Programmation dynamique à base d'agents× | Programmation dynamique stochastique× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1957 (DP); 1990s onward (ABM integration) | 1957 |
| Auteur d'origine≠ | Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Type≠ | Hybrid simulation-optimization | Sequential optimization under uncertainty |
| Source fondatrice | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Alias | ABDP, Agent-based DP, Multi-agent dynamic programming, ABM-DP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Apparentées≠ | 5 | 6 |
| Résumé≠ | 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. |
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