def __init__(self, question, testDict):
super(ApproximateQLearningTest, self).__init__(question, testDict)
self.discount = float(testDict['discount'])
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
if 'noise' in testDict: self.grid.setNoise(float(testDict['noise']))
if 'livingReward' in testDict:
self.grid.setLivingReward(float(testDict['livingReward']))
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict['epsilon'])
self.learningRate = float(testDict['learningRate'])
self.extractor = 'IdentityExtractor'
if 'extractor' in testDict:
self.extractor = testDict['extractor']
self.opts = {
'actionFn': self.env.getPossibleActions,
'epsilon': self.epsilon,
'gamma': self.discount,
'alpha': self.learningRate
}
numExperiences = int(testDict['numExperiences'])
maxPreExperiences = 10
self.numsExperiencesForDisplay = list(
range(min(numExperiences, maxPreExperiences)))
self.testOutFile = testDict['test_out_file']
if sys.platform == 'win32':
_, question_name, test_name = testDict['test_out_file'].split('\\')
else:
_, question_name, test_name = testDict['test_out_file'].split('/')
self.experiences = Experiences(test_name.split('.')[0])
if maxPreExperiences < numExperiences:
self.numsExperiencesForDisplay.append(numExperiences)
def __init__(self, question, testDict):
super(EpsilonGreedyTest, self).__init__(question, testDict)
self.discount = float(testDict['discount'])
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
if 'noise' in testDict: self.grid.setNoise(float(testDict['noise']))
if 'livingReward' in testDict:
self.grid.setLivingReward(float(testDict['livingReward']))
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict['epsilon'])
self.learningRate = float(testDict['learningRate'])
self.numExperiences = int(testDict['numExperiences'])
self.numIterations = int(testDict['iterations'])
self.opts = {
'actionFn': self.env.getPossibleActions,
'epsilon': self.epsilon,
'gamma': self.discount,
'alpha': self.learningRate
}
if sys.platform == 'win32':
_, question_name, test_name = testDict['test_out_file'].split('\\')
else:
_, question_name, test_name = testDict['test_out_file'].split('/')
self.experiences = Experiences(test_name.split('.')[0])
def __init__(self, question, testDict):
super(EpsilonGreedyTest, self).__init__(question, testDict)
self.discount = float(testDict["discount"])
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
if "noise" in testDict:
self.grid.setNoise(float(testDict["noise"]))
if "livingReward" in testDict:
self.grid.setLivingReward(float(testDict["livingReward"]))
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict["epsilon"])
self.learningRate = float(testDict["learningRate"])
self.numExperiences = int(testDict["numExperiences"])
self.numIterations = int(testDict["iterations"])
self.opts = {
"actionFn": self.env.getPossibleActions,
"epsilon": self.epsilon,
"gamma": self.discount,
"alpha": self.learningRate,
}
if sys.platform == "win32":
_, question_name, test_name = testDict["test_out_file"].split("\\")
else:
_, question_name, test_name = testDict["test_out_file"].split("/")
self.experiences = Experiences(test_name.split(".")[0])
def __init__(self, question, testDict):
super(QLearningTest, self).__init__(question, testDict)
self.discount = float(testDict["discount"])
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
if "noise" in testDict:
self.grid.setNoise(float(testDict["noise"]))
if "livingReward" in testDict:
self.grid.setLivingReward(float(testDict["livingReward"]))
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict["epsilon"])
self.learningRate = float(testDict["learningRate"])
self.opts = {
"actionFn": self.env.getPossibleActions,
"epsilon": self.epsilon,
"gamma": self.discount,
"alpha": self.learningRate,
}
numExperiences = int(testDict["numExperiences"])
maxPreExperiences = 10
self.numsExperiencesForDisplay = list(
range(min(numExperiences, maxPreExperiences)))
self.testOutFile = testDict["test_out_file"]
if sys.platform == "win32":
_, question_name, test_name = testDict["test_out_file"].split("\\")
else:
_, question_name, test_name = testDict["test_out_file"].split("/")
self.experiences = Experiences(test_name.split(".")[0])
if maxPreExperiences < numExperiences:
self.numsExperiencesForDisplay.append(numExperiences)
class EpsilonGreedyTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(EpsilonGreedyTest, self).__init__(question, testDict)
self.discount = float(testDict['discount'])
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
if 'noise' in testDict: self.grid.setNoise(float(testDict['noise']))
if 'livingReward' in testDict:
self.grid.setLivingReward(float(testDict['livingReward']))
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict['epsilon'])
self.learningRate = float(testDict['learningRate'])
self.numExperiences = int(testDict['numExperiences'])
self.numIterations = int(testDict['iterations'])
self.opts = {
'actionFn': self.env.getPossibleActions,
'epsilon': self.epsilon,
'gamma': self.discount,
'alpha': self.learningRate
}
if sys.platform == 'win32':
_, question_name, test_name = testDict['test_out_file'].split('\\')
else:
_, question_name, test_name = testDict['test_out_file'].split('/')
self.experiences = Experiences(test_name.split('.')[0])
def execute(self, grades, moduleDict, solutionDict):
if self.testEpsilonGreedy(moduleDict):
return self.testPass(grades)
else:
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
with open(filePath, 'w') as handle:
handle.write('# This is the solution file for %s.\n' % self.path)
handle.write('# File intentionally blank.\n')
return True
def runAgent(self, moduleDict):
agent = moduleDict['qlearningAgents'].QLearningAgent(**self.opts)
states = [
state for state in self.grid.getStates()
if len(self.grid.getPossibleActions(state)) > 0
]
states.sort()
for i in range(self.numExperiences):
lastExperience = self.experiences.get_experience()
agent.update(*lastExperience)
return agent
def testEpsilonGreedy(self, moduleDict, tolerance=0.025):
agent = self.runAgent(moduleDict)
for state in self.grid.getStates():
numLegalActions = len(agent.getLegalActions(state))
if numLegalActions <= 1:
continue
numGreedyChoices = 0
optimalAction = agent.computeActionFromQValues(state)
for iteration in range(self.numIterations):
# assume that their computeActionFromQValues implementation is correct (q4 tests this)
if agent.getAction(state) == optimalAction:
numGreedyChoices += 1
# e = epsilon, g = # greedy actions, n = numIterations, k = numLegalActions
# g = n * [(1-e) + e/k] -> e = (n - g) / (n - n/k)
empiricalEpsilonNumerator = self.numIterations - numGreedyChoices
empiricalEpsilonDenominator = self.numIterations - self.numIterations / float(
numLegalActions)
empiricalEpsilon = empiricalEpsilonNumerator / empiricalEpsilonDenominator
error = abs(empiricalEpsilon - self.epsilon)
if error > tolerance:
self.addMessage(
"Epsilon-greedy action selection is not correct.")
self.addMessage(
"Actual epsilon = %f; student empirical epsilon = %f; error = %f > tolerance = %f"
% (self.epsilon, empiricalEpsilon, error, tolerance))
return False
return True
class ApproximateQLearningTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(ApproximateQLearningTest, self).__init__(question, testDict)
self.discount = float(testDict['discount'])
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
if 'noise' in testDict: self.grid.setNoise(float(testDict['noise']))
if 'livingReward' in testDict:
self.grid.setLivingReward(float(testDict['livingReward']))
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict['epsilon'])
self.learningRate = float(testDict['learningRate'])
self.extractor = 'IdentityExtractor'
if 'extractor' in testDict:
self.extractor = testDict['extractor']
self.opts = {
'actionFn': self.env.getPossibleActions,
'epsilon': self.epsilon,
'gamma': self.discount,
'alpha': self.learningRate
}
numExperiences = int(testDict['numExperiences'])
maxPreExperiences = 10
self.numsExperiencesForDisplay = list(
range(min(numExperiences, maxPreExperiences)))
self.testOutFile = testDict['test_out_file']
if sys.platform == 'win32':
_, question_name, test_name = testDict['test_out_file'].split('\\')
else:
_, question_name, test_name = testDict['test_out_file'].split('/')
self.experiences = Experiences(test_name.split('.')[0])
if maxPreExperiences < numExperiences:
self.numsExperiencesForDisplay.append(numExperiences)
def writeFailureFile(self, string):
with open(self.testOutFile, 'w') as handle:
handle.write(string)
def removeFailureFileIfExists(self):
if os.path.exists(self.testOutFile):
os.remove(self.testOutFile)
def execute(self, grades, moduleDict, solutionDict):
failureOutputFileString = ''
failureOutputStdString = ''
for n in self.numsExperiencesForDisplay:
testPass, stdOutString, fileOutString = self.executeNExperiences(
grades, moduleDict, solutionDict, n)
failureOutputStdString += stdOutString
failureOutputFileString += fileOutString
if not testPass:
self.addMessage(failureOutputStdString)
self.addMessage(
'For more details to help you debug, see test output file %s\n\n'
% self.testOutFile)
self.writeFailureFile(failureOutputFileString)
return self.testFail(grades)
self.removeFailureFileIfExists()
return self.testPass(grades)
def executeNExperiences(self, grades, moduleDict, solutionDict, n):
testPass = True
qValuesPretty, weights, actions, lastExperience = self.runAgent(
moduleDict, n)
stdOutString = ''
fileOutString = "==================== Iteration %d ====================\n" % n
if lastExperience is not None:
fileOutString += "Agent observed the transition (startState = %s, action = %s, endState = %s, reward = %f)\n\n" % lastExperience
weightsKey = 'weights_k_%d' % n
if weights == eval(solutionDict[weightsKey]):
fileOutString += "Weights at iteration %d are correct." % n
fileOutString += " Student/correct solution:\n\n%s\n\n" % pp.pformat(
weights)
for action in actions:
qValuesKey = 'q_values_k_%d_action_%s' % (n, action)
qValues = qValuesPretty[action]
if self.comparePrettyValues(qValues, solutionDict[qValuesKey]):
fileOutString += "Q-Values at iteration %d for action '%s' are correct." % (
n, action)
fileOutString += " Student/correct solution:\n\t%s" % self.prettyValueSolutionString(
qValuesKey, qValues)
else:
testPass = False
outString = "Q-Values at iteration %d for action '%s' are NOT correct." % (
n, action)
outString += " Student solution:\n\t%s" % self.prettyValueSolutionString(
qValuesKey, qValues)
outString += " Correct solution:\n\t%s" % self.prettyValueSolutionString(
qValuesKey, solutionDict[qValuesKey])
stdOutString += outString
fileOutString += outString
return testPass, stdOutString, fileOutString
def writeSolution(self, moduleDict, filePath):
with open(filePath, 'w') as handle:
for n in self.numsExperiencesForDisplay:
qValuesPretty, weights, actions, _ = self.runAgent(
moduleDict, n)
handle.write(
self.prettyValueSolutionString('weights_k_%d' % n,
pp.pformat(weights)))
for action in actions:
handle.write(
self.prettyValueSolutionString(
'q_values_k_%d_action_%s' % (n, action),
qValuesPretty[action]))
return True
def runAgent(self, moduleDict, numExperiences):
agent = moduleDict['qlearningAgents'].ApproximateQAgent(
extractor=self.extractor, **self.opts)
states = [
state for state in self.grid.getStates()
if len(self.grid.getPossibleActions(state)) > 0
]
states.sort()
lastExperience = None
for i in range(numExperiences):
lastExperience = self.experiences.get_experience()
agent.update(*lastExperience)
actions = list(
reduce(lambda a, b: set(a).union(b),
[self.grid.getPossibleActions(state) for state in states]))
qValues = {}
weights = agent.getWeights()
for state in states:
possibleActions = self.grid.getPossibleActions(state)
for action in actions:
if action not in qValues:
qValues[action] = {}
if action in possibleActions:
qValues[action][state] = agent.getQValue(state, action)
else:
qValues[action][state] = None
qValuesPretty = {}
for action in actions:
qValuesPretty[action] = self.prettyValues(qValues[action])
return (qValuesPretty, weights, actions, lastExperience)
def prettyPrint(self, elements, formatString):
pretty = ''
states = self.grid.getStates()
for ybar in range(self.grid.grid.height):
y = self.grid.grid.height - 1 - ybar
row = []
for x in range(self.grid.grid.width):
if (x, y) in states:
value = elements[(x, y)]
if value is None:
row.append(' illegal')
else:
row.append(formatString.format(elements[(x, y)]))
else:
row.append('_' * 10)
pretty += ' %s\n' % (" ".join(row), )
pretty += '\n'
return pretty
def prettyValues(self, values):
return self.prettyPrint(values, '{0:10.4f}')
def prettyPolicy(self, policy):
return self.prettyPrint(policy, '{0:10s}')
def prettyValueSolutionString(self, name, pretty):
return '%s: """\n%s\n"""\n\n' % (name, pretty.rstrip())
def comparePrettyValues(self, aPretty, bPretty, tolerance=0.01):
aList = self.parsePrettyValues(aPretty)
bList = self.parsePrettyValues(bPretty)
if len(aList) != len(bList):
return False
for a, b in zip(aList, bList):
try:
aNum = float(a)
bNum = float(b)
# error = abs((aNum - bNum) / ((aNum + bNum) / 2.0))
error = abs(aNum - bNum)
if error > tolerance:
return False
except ValueError:
if a.strip() != b.strip():
return False
return True
def parsePrettyValues(self, pretty):
values = pretty.split()
return values
class QLearningTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(QLearningTest, self).__init__(question, testDict)
self.discount = float(testDict["discount"])
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
if "noise" in testDict:
self.grid.setNoise(float(testDict["noise"]))
if "livingReward" in testDict:
self.grid.setLivingReward(float(testDict["livingReward"]))
self.grid = gridworld.Gridworld(parseGrid(testDict["grid"]))
self.env = gridworld.GridworldEnvironment(self.grid)
self.epsilon = float(testDict["epsilon"])
self.learningRate = float(testDict["learningRate"])
self.opts = {
"actionFn": self.env.getPossibleActions,
"epsilon": self.epsilon,
"gamma": self.discount,
"alpha": self.learningRate,
}
numExperiences = int(testDict["numExperiences"])
maxPreExperiences = 10
self.numsExperiencesForDisplay = list(
range(min(numExperiences, maxPreExperiences)))
self.testOutFile = testDict["test_out_file"]
if sys.platform == "win32":
_, question_name, test_name = testDict["test_out_file"].split("\\")
else:
_, question_name, test_name = testDict["test_out_file"].split("/")
self.experiences = Experiences(test_name.split(".")[0])
if maxPreExperiences < numExperiences:
self.numsExperiencesForDisplay.append(numExperiences)
def writeFailureFile(self, string):
with open(self.testOutFile, "w") as handle:
handle.write(string)
def removeFailureFileIfExists(self):
if os.path.exists(self.testOutFile):
os.remove(self.testOutFile)
def execute(self, grades, moduleDict, solutionDict):
failureOutputFileString = ""
failureOutputStdString = ""
for n in self.numsExperiencesForDisplay:
checkValuesAndPolicy = n == self.numsExperiencesForDisplay[-1]
testPass, stdOutString, fileOutString = self.executeNExperiences(
grades, moduleDict, solutionDict, n, checkValuesAndPolicy)
failureOutputStdString += stdOutString
failureOutputFileString += fileOutString
if not testPass:
self.addMessage(failureOutputStdString)
self.addMessage(
"For more details to help you debug, see test output file %s\n\n"
% self.testOutFile)
self.writeFailureFile(failureOutputFileString)
return self.testFail(grades)
self.removeFailureFileIfExists()
return self.testPass(grades)
def executeNExperiences(self, grades, moduleDict, solutionDict, n,
checkValuesAndPolicy):
testPass = True
(
valuesPretty,
qValuesPretty,
actions,
policyPretty,
lastExperience,
) = self.runAgent(moduleDict, n)
stdOutString = ""
# fileOutString = "==================== Iteration %d ====================\n" % n
fileOutString = ""
if lastExperience is not None:
# fileOutString += "Agent observed the transition (startState = %s, action = %s, endState = %s, reward = %f)\n\n\n" % lastExperience
pass
for action in actions:
qValuesKey = "q_values_k_%d_action_%s" % (n, action)
qValues = qValuesPretty[action]
if self.comparePrettyValues(qValues, solutionDict[qValuesKey]):
# fileOutString += "Q-Values at iteration %d for action '%s' are correct." % (n, action)
# fileOutString += " Student/correct solution:\n\t%s" % self.prettyValueSolutionString(qValuesKey, qValues)
pass
else:
testPass = False
outString = (
"Q-Values at iteration %d for action '%s' are NOT correct."
% (n, action))
outString += (
" Student solution:\n\t%s" %
self.prettyValueSolutionString(qValuesKey, qValues))
outString += (" Correct solution:\n\t%s" %
self.prettyValueSolutionString(
qValuesKey, solutionDict[qValuesKey]))
stdOutString += outString
fileOutString += outString
if checkValuesAndPolicy:
if not self.comparePrettyValues(valuesPretty,
solutionDict["values"]):
testPass = False
outString = "Values are NOT correct."
outString += (
" Student solution:\n\t%s" %
self.prettyValueSolutionString("values", valuesPretty))
outString += (" Correct solution:\n\t%s" %
self.prettyValueSolutionString(
"values", solutionDict["values"]))
stdOutString += outString
fileOutString += outString
if not self.comparePrettyValues(policyPretty,
solutionDict["policy"]):
testPass = False
outString = "Policy is NOT correct."
outString += (
" Student solution:\n\t%s" %
self.prettyValueSolutionString("policy", policyPretty))
outString += (" Correct solution:\n\t%s" %
self.prettyValueSolutionString(
"policy", solutionDict["policy"]))
stdOutString += outString
fileOutString += outString
return testPass, stdOutString, fileOutString
def writeSolution(self, moduleDict, filePath):
with open(filePath, "w") as handle:
valuesPretty = ""
policyPretty = ""
for n in self.numsExperiencesForDisplay:
(
valuesPretty,
qValuesPretty,
actions,
policyPretty,
_,
) = self.runAgent(moduleDict, n)
for action in actions:
handle.write(
self.prettyValueSolutionString(
"q_values_k_%d_action_%s" % (n, action),
qValuesPretty[action],
))
handle.write(self.prettyValueSolutionString(
"values", valuesPretty))
handle.write(self.prettyValueSolutionString(
"policy", policyPretty))
return True
def runAgent(self, moduleDict, numExperiences):
agent = moduleDict["qlearningAgents"].QLearningAgent(**self.opts)
states = [
state for state in self.grid.getStates()
if len(self.grid.getPossibleActions(state)) > 0
]
states.sort()
lastExperience = None
for i in range(numExperiences):
lastExperience = self.experiences.get_experience()
agent.update(*lastExperience)
actions = list(
reduce(
lambda a, b: set(a).union(b),
[self.grid.getPossibleActions(state) for state in states],
))
values = {}
qValues = {}
policy = {}
for state in states:
values[state] = agent.computeValueFromQValues(state)
policy[state] = agent.computeActionFromQValues(state)
possibleActions = self.grid.getPossibleActions(state)
for action in actions:
if action not in qValues:
qValues[action] = {}
if action in possibleActions:
qValues[action][state] = agent.getQValue(state, action)
else:
qValues[action][state] = None
valuesPretty = self.prettyValues(values)
policyPretty = self.prettyPolicy(policy)
qValuesPretty = {}
for action in actions:
qValuesPretty[action] = self.prettyValues(qValues[action])
return (
valuesPretty,
qValuesPretty,
actions,
policyPretty,
lastExperience,
)
def prettyPrint(self, elements, formatString):
pretty = ""
states = self.grid.getStates()
for ybar in range(self.grid.grid.height):
y = self.grid.grid.height - 1 - ybar
row = []
for x in range(self.grid.grid.width):
if (x, y) in states:
value = elements[(x, y)]
if value is None:
row.append(" illegal")
else:
row.append(formatString.format(elements[(x, y)]))
else:
row.append("_" * 10)
pretty += " %s\n" % (" ".join(row), )
pretty += "\n"
return pretty
def prettyValues(self, values):
return self.prettyPrint(values, "{0:10.4f}")
def prettyPolicy(self, policy):
return self.prettyPrint(policy, "{0:10s}")
def prettyValueSolutionString(self, name, pretty):
return '%s: """\n%s\n"""\n\n' % (name, pretty.rstrip())
def comparePrettyValues(self, aPretty, bPretty, tolerance=0.01):
aList = self.parsePrettyValues(aPretty)
bList = self.parsePrettyValues(bPretty)
if len(aList) != len(bList):
return False
for a, b in zip(aList, bList):
try:
aNum = float(a)
bNum = float(b)
# error = abs((aNum - bNum) / ((aNum + bNum) / 2.0))
error = abs(aNum - bNum)
if error > tolerance:
return False
except ValueError:
if a.strip() != b.strip():
return False
return True
def parsePrettyValues(self, pretty):
values = pretty.split()
return values
