> For the complete documentation index, see [llms.txt](https://theshank.gitbook.io/ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://theshank.gitbook.io/ai/reinforcement-learning/untitled/chapter-3-finite-markov-decision-processes.md).
# Chapter 3: Finite Markov Decision Processes
* You take a action a\_t at a time-step in state s\_t and in response to that action you get response(reward) r\_t+1 just after that, in next time step and also get into new state s\_t+1.
* S0, A0, R1, S1, A1, R2, S2, A2, R3, . . . this sequence defines a MDP.
* Markov Property:- Future is independent of the past given the present.
* Finite MDP - Have finite element in S,A,R set
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