def nextstate(state, price):
disidx = dfs(state)
t = tfs(state)
nextdis = min(
modelt4.cocoef * disstates[disidx] + modelt4.phi(t, customer, price),
disstates[-1])
return getstate(t + 1, nextdis)
def actionablestatesfrom(state):
global maxdisseen
t = tfs(state)
nextdisvalues = np.round(
[modelt4.cocoef*disstates[dfs(state)]+modelt4.phi(t,customer,a) for a in actions if a >= modelt4.wholeprice(t)], -2)
maxdisseen = max(maxdisseen, max(nextdisvalues))
nextdisvalues = [min(value, disstates[-1]) for value in nextdisvalues]
idx = getdisstate(nextdisvalues)
return list(set([(t) + i * (ntimeslots+1) for i in idx]))
#dis = [modelt4.phi(t,customer,retailprices[t-1]) for t in range(1,25)]
bestpolicy = list()
dislist = list()
policystate = initstate
action = np.argmax(qmatrix[policystate,:])
price = actions[action]
dislist.append(initdis)
bestpolicy.append(price)
cumdislist = [dislist[0]]
sar = [(policystate, price, reward(policystate, customer, price))]
#policystate = np.argmax(qmatrix[:,:], axis=1)
for t in range(2,25):
policystate = nextstate(policystate, price)
action = np.argmax(qmatrix[policystate,:])
price = actions[action]
dislist.append(modelt4.phi(t,customer,price))
cumdislist.append(disstates[dfs(policystate)])
bestpolicy.append(price)
sar.append((policystate, price, reward(policystate, customer, price)))
print(bestpolicy)
# VISUALISE INPUT AND OUTPUT DATA
# Combined plot
def plotresults():
import matplotlib.pyplot as plt
trange = list(range(1,25))
barw = 0.25