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# This file may not be shared/redistributed without permission. Please read copyright notice in the git repo. If this file contains other copyright notices disregard this text.
"""
References:
[Her25] Tue Herlau. Sequential decision making. (Freely available online), 2025.
"""
r"""
Implements the inventory-control problem from (Her25, Subsection 5.1.2).
"""
from irlc.ex02.dp_model import DPModel
from irlc.ex02.dp import DP_stochastic
class InventoryDPModel(DPModel):
def __init__(self, N=3):
super().__init__(N=N)
def A(self, x, k): # Action space A_k(x)
return {0, 1, 2}
def S(self, k): # State space S_k
return {0, 1, 2}
def g(self, x, u, w, k): # Cost function g_k(x,u,w)
return u + (x + u - w) ** 2
def f(self, x, u, w, k): # Dynamics f_k(x,u,w)
return max(0, min(2, x + u - w ))
def Pw(self, x, u, k): # Distribution over random disturbances
# TODO: 1 lines missing.
raise NotImplementedError("Implement function body")
def gN(self, x):
return 0
def main():
inv = InventoryDPModel()
J,pi = DP_stochastic(inv)
print(f"Inventory control optimal policy/value functions")
for k in range(inv.N):
print(", ".join([f" J_{k}(x_{k}={i}) = {J[k][i]:.2f}" for i in inv.S(k)] ) )
for k in range(inv.N):
print(", ".join([f"pi_{k}(x_{k}={i}) = {pi[k][i]}" for i in inv.S(k)] ) )
if __name__ == "__main__":
main()