def Neural(env, params, prevTransition, sess, qnA, qnB):
nextCandidates = prevTransition.nextCandidates.copy()
nextVisited = prevTransition.nextVisited.copy()
qValues, maxQ, action, link, reward = NeuralWalk(env, params,
nextCandidates,
nextVisited, sess, qnA)
assert (link is not None)
assert (qValues.shape[1] > 0)
#print("qValues", qValues.shape, action, prevTransition.nextCandidates.Count(), nextCandidates.Count())
nextCandidates.Group(nextVisited)
# calc nextMaxQ
if nextCandidates.Count() > 0:
# links to follow NEXT
_, _, nextAction = qnA.PredictAll(env, sess, params.langIds,
nextVisited, nextCandidates)
#print("nextAction", nextAction, nextLangRequested, nextCandidates.Debug())
nextQValuesB, _, _ = qnB.PredictAll(env, sess, params.langIds,
nextVisited, nextCandidates)
nextMaxQ = nextQValuesB[0, nextAction]
#print("nextMaxQ", nextMaxQ, nextMaxQB, nextQValuesA[0, nextAction])
else:
nextMaxQ = 0
newVal = reward + params.gamma * nextMaxQ
targetQ = (1 - params.alpha) * maxQ + params.alpha * newVal
qValues[0, action] = targetQ
transition = Transition(env, action, link, params.langIds, qValues,
prevTransition.nextVisited,
prevTransition.nextCandidates, nextVisited,
nextCandidates)
return transition, reward
def Neural(env, params, prevTransition, sess, qn):
nextCandidates = prevTransition.nextCandidates.copy()
nextVisited = prevTransition.nextVisited.copy()
action, link, reward = NeuralWalk(env, params, params.eps, nextCandidates,
nextVisited, sess, qn)
assert (link is not None)
#print("qValues", qValues.shape, action, prevTransition.nextCandidates.Count(), nextCandidates.Count())
nextCandidates.Group(nextVisited)
transition = Transition(env, action, reward, link, params.langIds,
prevTransition.nextVisited,
prevTransition.nextCandidates, nextVisited,
nextCandidates)
return transition, reward
def Walk(env, params, sess, qns):
ret = []
visited = set()
langsVisited = np.zeros([1, 3]) # langId -> count
candidates = Candidates(params, env)
node = env.nodes[sys.maxsize]
#stopNode = env.nodes[0]
#link = Link("", 0, stopNode, stopNode)
#candidates.AddLink(link)
mainStr = "lang:" + str(node.lang)
rewardStr = "rewards:"
actionStr = "actions:"
i = 0
numAligned = 0
totReward = 0.0
totDiscountedReward = 0.0
discount = 1.0
while True:
qnA = qns.q[0]
assert (node.urlId not in visited)
#print("node", node.Debug())
visited.add(node.urlId)
#print("node.lang", node.lang, langsVisited.shape)
UpdateLangsVisited(langsVisited, node, params.langIds)
#print(" langsVisited", langsVisited)
candidates.AddLinks(node, visited, params)
numParallelDocs = NumParallelDocs(env, visited)
ret.append(numParallelDocs)
#print("candidates", candidates.Debug())
_, _, _, _, _, _, _, _, action, link, reward = NeuralWalk(
env, params, 0.0, candidates, visited, langsVisited, sess, qnA)
node = link.childNode
#print("action", action, qValues)
actionStr += str(action) + " "
totReward += reward
totDiscountedReward += discount * reward
mainStr += "->" + str(node.lang)
rewardStr += "->" + str(reward)
if node.alignedNode is not None:
mainStr += "*"
numAligned += 1
discount *= params.gamma
i += 1
if node.urlId == 0:
break
if len(visited) > params.maxDocs:
break
mainStr += " " + str(i)
rewardStr += " " + str(totReward) + "/" + str(totDiscountedReward)
print(actionStr)
print(mainStr)
print(rewardStr)
return ret, totReward, totDiscountedReward
def Neural(env, params, candidates, visited, langsVisited, sess, qnA, qnB):
numActions, linkLang, mask, numSiblings, numVisitedSiblings, numMatchedSiblings, qValues, maxQ, action, link, reward = NeuralWalk(
env, params, params.eps, candidates, visited, langsVisited, sess, qnA)
assert (link is not None)
# calc nextMaxQ
nextVisited = visited.copy()
nextVisited.add(link.childNode.urlId)
nextCandidates = candidates.copy()
nextCandidates.AddLinks(link.childNode, nextVisited, params)
nextLangsVisited = langsVisited.copy()
UpdateLangsVisited(nextLangsVisited, link.childNode, params.langIds)
if nextCandidates.Count() > 0:
_, _, _, _, _, _, _, _, nextAction = qnA.PredictAll(
env, sess, params.langIds, nextLangsVisited, nextCandidates)
#print("nextAction", nextAction, nextLangRequested, nextCandidates.Debug())
_, _, _, _, _, _, nextQValuesB, _, _ = qnB.PredictAll(
env, sess, params.langIds, nextLangsVisited, nextCandidates)
nextMaxQ = nextQValuesB[0, nextAction]
#print("nextMaxQ", nextMaxQ, nextMaxQB, nextQValuesA[0, nextAction])
else:
nextMaxQ = 0
newVal = reward + params.gamma * nextMaxQ
targetQ = (1 - params.alpha) * maxQ + params.alpha * newVal
qValues[0, action] = targetQ
transition = Transition(link.parentNode.urlId, link.childNode.urlId,
numActions, linkLang, mask, numSiblings,
numVisitedSiblings, numMatchedSiblings,
params.langIds, langsVisited, qValues)
return transition
