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Bellman's Optimality Principle

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Theorem
StochasticControl

Theorem

Consider the finite horizon optimization problem J(X,γ)=Exγ[āˆ‘k=0Nāˆ’1c(Xk,Uk)+cN(XN)]J(X,\gamma)=E_{x}^{\gamma}\left[ \sum_{k=0}^{N-1}c(X_{k},U_{k})+c_{N}(X_{N}) \right]If ∃J0,…,JNāˆ’1,f0,…,fNāˆ’1\exists J_{0},\dots,J_{N-1},f_{0},\dots,f_{N-1} where JN(x)=cN(x)J_{N}(x)=c_{N}(x)and for 0≤t≤Nāˆ’10\le t\le N-1 Jt(x)=min⁔u∈U(c(x,u)+E[Jt+1(xt+1)∣xt=x,ut=u])=c(x,ft(x))+E[Jt+1(xt+1)∣xt=x,ut=ft(x)]\begin{align*} J_{t}(x)&=\min_{u\in\mathbb{U}}(c(x,u)+E[J_{t+1}(x_{t+1})|x_{t}=x,u_{t}=u])\\ &=c(x,f_{t}(x))+E[J_{t+1}(x_{t+1})|x_{t}=x,u_{t}=f_{t}(x)] \end{align*}then we have that infā”Ī³āˆˆĪ“AJN(x)=J0(x)\inf_{\gamma\in\Gamma_{A}}J_{N}(x)=J_{0}(x)and Ī³āˆ—={f0,…,fNāˆ’1}\gamma^{*}=\{ f_{0},\dots,f_{N-1} \} is optimal.