Methoden vergleichen

	Agent-based dynamic programming	Reinforcement Learning
Fachgebiet	Simulation	Deep Learning
Familie	Process / pipeline	Machine learning
Entstehungsjahr	1957 (DP); 1990s onward (ABM integration)	1950s–1998
Urheber	Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Typ	Hybrid simulation-optimization	Sequential decision-making framework
Wegweisende Quelle	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Aliasnamen	ABDP, Agent-based DP, Multi-agent dynamic programming, ABM-DP	RL, reward-based learning, trial-and-error learning, policy optimization
Verwandt	5	2
Zusammenfassung	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.	Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.