def unitTest(cls):
print "Testing ValueIteration"
np.random.seed(0)
from system import System
from networks import genGridNetwork
from model import PJ
system = System(genGridNetwork((3,3)),PJ())
numNodes = system.network.numNodes
numTrt = Agent.numTrt(system)
numValidTrt = Agent.numValidTrt(numNodes,numTrt)
v = ValueIteration.solve(dc(system))
q = PolicyIteration.solve(dc(system))
q = util.unflattenQ(q,numNodes,numValidTrt)
vChk = [max(i) for i in q]
for i in zip(v,vChk):
print "% 12.6f % 10.6f" % i
def unitTest(cls):
print "Testing PolicyIteration"
np.random.seed(0)
from networks import genGridNetwork
from model import PJ
from copy import deepcopy
from runners import vPiS
system = systems.System(genGridNetwork((2, 2)), PJ())
numNodes = system.network.numNodes
p = np.array(cls.calcP(system))
r = np.array(cls.calcR(system))
one = np.ones((p.shape[1],))
pRowsum = np.dot(p, one)
## check numerical ranges on p
tol = 1e-8
if np.amin(p) < 0:
raise ValueError("P has negative values")
if np.amax(p) > 1.0:
raise ValueError("P has values greater than 1")
if abs(max(pRowsum) - 1) > tol or abs(min(pRowsum) - 1) > tol:
raise ValueError("Not all row sums for P are 1.0")
numTrt = agents.Agent.numTrt(system)
numValidTrt = agents.Agent.numValidTrt(numNodes, numTrt)
q = util.unflattenQ(np.random.randint(numNodes, size=(1 << numNodes) * numValidTrt), numNodes, numValidTrt)
pi = cls.piForPolicy(util.q2Policy(q), system)
one = np.ones((pi.shape[1],))
piRowSum = pi * one
## check numerical ranges on pi
if pi.max() < 0:
raise ValueError("Pi has some negative values")
if pi.min() > 0:
raise ValueError("Pi has values greater than 1")
if abs(np.amin(piRowSum) - 1) > tol or abs(np.amax(piRowSum) - 1):
raise ValueError("Rows of pi do not sum to 1")
## make sure random agent estimates worse Q-values
gamma = 0.9
randPol = [range(numValidTrt) for i in range(1 << numNodes)]
vRa = PolicyIteration2.vForPolicy(randPol, system, gamma=gamma).tolist()
polOpt = PolicyIteration2.solve(deepcopy(system), gamma=gamma)
vOpt = PolicyIteration2.vForPolicy(polOpt, system, gamma=gamma).tolist()
cnt = sum(i > j for i, j in zip(vRa, vOpt))
if cnt > 0:
raise ValueError("Random Agent does better " + "than optimal V-function %d times" % cnt)
## check that gamma = 0 results in a v-function equal to
## expected immediate rewards
gamma = 0.0
polOpt = PolicyIteration2.solve(deepcopy(system), gamma=gamma)
vOpt = PolicyIteration2.vForPolicy(polOpt, system, gamma=gamma)
pi = cls.piForPolicy(polOpt, system)
if np.linalg.norm(pi.dot(r) - vOpt, 2) > 1e-10:
raise ValueError("Gamma = 0 did not result in expected " + "immediate rewards")
## check analytical values with simulated values
gamma = 0.5
polOpt = PolicyIteration2.solve(deepcopy(system), gamma=gamma)
vOpt = PolicyIteration2.vForPolicy(polOpt, system, gamma=gamma)
agentOpt = agents.PolAgent.fromPol(polOpt)
diff = 0.0
for s in range(1 << numNodes):
print "Checking state % 4d" % s
val = vPiS(s, system, agentOpt, gamma=gamma, finalT=10, reps=1000)
diff += abs(vOpt[s] - val)
diff /= float(1 << numNodes)
# print "diff from sim: % 10.6f" % diff
if diff > 0.05:
raise ValueError("V values differ from sim by %f" % diff)
