ScholarGate
Asisten

Bandingkan metode

Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.

Pemrograman Dinamis Berbasis Agen×Pemrograman Dinamis Stokastik×
BidangSimulasiSimulasi
KeluargaProcess / pipelineProcess / pipeline
Tahun asal1957 (DP); 1990s onward (ABM integration)1957
PencetusBellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration)Bellman, R.; formalized for stochastic settings by Puterman, M. L.
TipeHybrid simulation-optimizationSequential optimization under uncertainty
Sumber perintisBellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
AliasABDP, Agent-based DP, Multi-agent dynamic programming, ABM-DPSDP, Markov Decision Process, MDP, Stochastic DP
Terkait56
RingkasanAgent-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.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED

Ke halaman pencarian Download slides

ScholarGateBandingkan metode: Agent-based dynamic programming · Stochastic Dynamic Programming. Diakses 2026-06-15 dari https://scholargate.app/id/compare