# Sequential decision making

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**Sequential decision making** is a concept in [control theory](/source/Control_theory) and [operations research](/source/Operations_research), which involves making a series of decisions over time to optimize an [objective function](/source/Objective_function), such as maximizing cumulative rewards or minimizing costs. In this framework, each decision influences subsequent choices and system outcomes, taking into account the current state, available actions, and the [probabilistic nature](/source/Stochastic) of [state transitions](/source/Transition_system).[1] This process is used for modeling and regulation of [dynamic systems](/source/Dynamical_system), especially under uncertainty, and is commonly addressed using methods like [Markov decision processes](/source/Markov_decision_process) (MDPs) and [dynamic programming](/source/Dynamic_programming).[2]

## References

1. Puterman, Martin L. (1994). *Markov decision processes: discrete stochastic dynamic programming*. Wiley series in probability and mathematical statistics. Applied probability and statistics section. New York: Wiley. pp. 1–2. ISBN 978-0-471-61977-2.

1. Bellman, Richard (1958-09-01). ["Dynamic programming and stochastic control processes"](https://linkinghub.elsevier.com/retrieve/pii/S0019995858800030). *Information and Control*. **1** (3): 228–239. [doi:10.1016/S0019-9958(58)80003-0](https://doi.org/10.1016/S0019-9958(58)80003-0). [ISSN 0019-9958](https://www.worldcat.org/issn/0019-9958)

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