def main(network):
print "network: " + network.kind
## reset
np.random.seed(0)
random.seed(0)
## setup system
model = PJ()
system = systems.System(dc(network),dc(model))
numNodes = network.numNodes
numTrt = agents.Agent.numTrt(system)
numValidTrt = agents.Agent.numValidTrt(numNodes,numTrt)
gamma = 0.9
## function approx agent
qfa = QFnApprox(features.Features3(),agents.MyopicAgent())
qfa.startCvGroups(10)
## epsilon greedy agent
eps = 0.1
epsAgent = agents.EpsAgent.fromAgent(agents.MyopicAgent(),eps)
## estimate policies
itersPerRep = 1000
for i in range(1000):
## function approx
qfa.iterAndSolve(dc(system),epsAgent,nIter=itersPerRep,gamma=gamma,
addToCvGroups=True,ridgePen=True)
np.savetxt(dataDump.dd.open("beta"+str((i+1)*itersPerRep)+".csv","w"),
qfa.beta,delimiter=",")
print "Saved beta with % 10d iters" % ((i+1)*itersPerRep)
dataDump.dd.clean(force=True)
def main(network, nIters):
print "network: " + network.kind
## reset
np.random.seed(0)
random.seed(0)
## setup system
model = PJ()
system = systems.System(dc(network), dc(model))
numNodes = network.numNodes
numTrt = agents.Agent.numTrt(system)
numValidTrt = agents.Agent.numValidTrt(numNodes, numTrt)
gamma = 0.9
## no treatment and random agents
compAgents = {}
compAgents["noTrt"] = agents.NoTrtAgent()
compAgents["random"] = agents.RandomAgent()
compAgents["myopic"] = agents.MyopicAgent()
compAgents["proximal"] = agents.ProximalAgent()
## optimal agent
optPol = PolicyIteration2.solve(dc(system), gamma=gamma)
optAgent = agents.PolAgent.fromPol(optPol)
compAgents["optimal"] = optAgent
eps = 0.1
## sarsa agent
qSarsa = QSarsa(dc(system), gamma=gamma, lam=0.5, eps=eps)
## function approx agent
# qfa = QFnApprox(features.Features2(),dc(optAgent))
qfa = QFnApprox(features.Features3(), agents.MyopicAgent())
qfa.startCvGroups(10)
## epsilon greedy agent
# epsAgent = agents.EpsAgent.fromAgent(optAgent,eps)
epsAgent = agents.EpsAgent.fromAgent(agents.MyopicAgent(), eps)
## estimate policies
past = 0
for nIt in nIters:
print "iters: % 8d" % nIt
## sarsa
qSarsa.iter(nIt - past)
name = "qSarsa" + str(nIt)
compAgents[name] = agents.QAgent.fromQ(qSarsa.q)
## function approx
qfa.iterAndSolve(dc(system), epsAgent, nIter=nIt - past, gamma=gamma, addToCvGroups=True, ridgePen=True)
name = "qFnApprox" + str(nIt)
compAgents[name] = agents.QAgent.fromQ(qfa.getQ(dc(system)))
## step-wise greedy
name = "swGreedy" + str(nIt)
compAgents[name] = agents.SwGreedyAgent.fromFn(dc(qfa.qHatSA))
past = nIt
results = compareAgents(system, compAgents, 1000, 100, 1.0)
resSort = []
for a in results:
resSort.append((results[a]["mean"], results[a]["stdev"], a))
resSort = sorted(resSort, reverse=True)
print "% 24s % 16s % 16s" % ("Agent", "Mean", "Stdev")
for r in resSort:
print "% 24s % 16.7f % 16.7f" % (r[2], r[0], r[1])
resFile = "results_" + network.kind + ".txt"
dataDump.dd("Agent,Mean,Stdev\n", resFile, "w")
for res in resSort:
dataDump.dd(",".join(map(str, (res[2], res[0], res[1]))) + "\n", resFile, "a")
