根据压缩映射定理(Contraction Mapping Theorem),从任意一点V0(k)出发,经过n次的压缩映射后,Vn(k)=TnV0(k)存在极限,且该极限是一个不动点。因此,依据上述理论,可以使用值函数迭代(Value Function Iteration)的方法来数值求解上述动态规划问题。 这里给出一个基本的值函数迭代的MATLAB代码: 说
Value Function Iteration 1 Value Function IterationFor, Opecky
ValueFunctionIteration 1ValueFunctionIteration Considerthestandardneoclassicalgrowthmodelinrecursiveform, V(K)=max C,K {U(C)+βV(K )} subjectto C+K =zF(K,1)+(1−δ)K, C≥0, K ≥0, K 0 given whereU(·)andF(·,·)satisfythestandardassumptions,0<β<1and0≤δ≤1,andzis assumedto...
1.3K Downloads Updated16 Apr 2025 View License on GitHub Share Open in MATLAB Online Download A Matlab Toolkit for Macroeconomic Models using Value Function Iteration. Automatically parallelizes on CPUs and GPU. Includes commands for simulating time series and stationary distributions, and on evaluating...
利用贝尔曼方程(Bellman Equation)作为迭代公式,通过迭代得到任意策略的价值函数(Value Function) (1) 把上式改写成下式,即可作为迭代公式 (2) 到这里可能有些人不明白这个迭代的具体形式是什么样的,其实写成向量方程的形式就更容易理解了 (3) 即s,s′都是状态空间S的状态,不停地利用上式进行迭代即可得到vπ的...
Each value function iteration step iVi(k,z)=maxk′∈K︸Peak-FindingF(k,z,k′)+β·∑z′Vi−1(k′,z′)Q(z′,z)︸ObjectiveFunctionv(k,z,k′)involves two distinct operations: 1) the evaluation of the objective function, v(k, z, k′); and 2) the peak-finding algorithm. The...
Value_Function_Iteration_Example_Matlab/calculateConsumption.m Go to file Copy path 15 lines (12 sloc)613 Bytes RawBlame function[c1,c2] =calculateConsumption(alpha,delta,kVals,zVals,aVals,policyK,policyL1,policyL2,policyC1,policyC2) %Calculate consumption choices ...
A Matlab Toolkit for Macroeconomic Models using Value Function Iteration Website:vfitoolkit.com Documentation, Examples, Instructions on Getting Started, and more are all on the website. Questions? Ask at the forum:discourse.vfitoolkit.com
证明 Value Iteration 的收敛性。背景:Policy Iteration:先得到一个 policy,然后算出它的 value function,基于该 value function 取 max_a Q(s,a) 作为新 policy,再算新 policy 的 value function… Value Iteration:不停对 value function 使用贝尔曼算子,新 V = max_a [r(s,a) + γV(s')]。
输出:value function Control(策略控制,即寻找最优策略) 输入:MDP$<S, A, P, R, \gamma> $ 输出:最优的价值函数 和最优的策略 一、策略评估 首先,我们来看如何使用动态规划来求解强化学习的预测问题,即求解给定策略的状态价值函数的问题。这个问题的求解过程我们通常叫做策略评估(Policy evaluation) ...