reinforcementTestClasses.py (original)
# reinforcementTestClasses.py
# ---------------------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
import testClasses
import random, math, traceback, sys, os
import layout, textDisplay, pacman, gridworld
import time
from util import Counter, TimeoutFunction, FixedRandom
from collections import defaultdict
from pprint import PrettyPrinter
from hashlib import sha1
pp = PrettyPrinter()
VERBOSE = False
import gridworld
LIVINGREWARD = -0.1
NOISE = 0.2
class ValueIterationTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(ValueIterationTest, self).__init__(question, testDict)
self.discount = float(testDict['discount'])
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
iterations = int(testDict['valueIterations'])
if 'noise' in testDict: self.grid.setNoise(float(testDict['noise']))
if 'livingReward' in testDict: self.grid.setLivingReward(float(testDict['livingReward']))
maxPreIterations = 10
self.numsIterationsForDisplay = range(min(iterations, maxPreIterations))
self.testOutFile = testDict['test_out_file']
if maxPreIterations < iterations:
self.numsIterationsForDisplay.append(iterations)
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.numsIterationsForDisplay:
checkPolicy = (n == self.numsIterationsForDisplay[-1])
testPass, stdOutString, fileOutString = self.executeNIterations(grades, moduleDict, solutionDict, n, checkPolicy)
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 executeNIterations(self, grades, moduleDict, solutionDict, n, checkPolicy):
testPass = True
valuesPretty, qValuesPretty, actions, policyPretty = self.runAgent(moduleDict, n)
stdOutString = ''
fileOutString = ''
valuesKey = "values_k_%d" % n
if self.comparePrettyValues(valuesPretty, solutionDict[valuesKey]):
fileOutString += "Values at iteration %d are correct.\n" % n
fileOutString += " Student/correct solution:\n %s\n" % self.prettyValueSolutionString(valuesKey, valuesPretty)
else:
testPass = False
outString = "Values at iteration %d are NOT correct.\n" % n
outString += " Student solution:\n %s\n" % self.prettyValueSolutionString(valuesKey, valuesPretty)
outString += " Correct solution:\n %s\n" % self.prettyValueSolutionString(valuesKey, solutionDict[valuesKey])
stdOutString += outString
fileOutString += outString
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" % (n, action)
fileOutString += " Student/correct solution:\n %s\n" % self.prettyValueSolutionString(qValuesKey, qValues)
else:
testPass = False
outString = "Q-Values at iteration %d for action %s are NOT correct.\n" % (n, action)
outString += " Student solution:\n %s\n" % self.prettyValueSolutionString(qValuesKey, qValues)
outString += " Correct solution:\n %s\n" % self.prettyValueSolutionString(qValuesKey, solutionDict[qValuesKey])
stdOutString += outString
fileOutString += outString
if checkPolicy:
if not self.comparePrettyValues(policyPretty, solutionDict['policy']):
testPass = False
outString = "Policy is NOT correct.\n"
outString += " Student solution:\n %s\n" % self.prettyValueSolutionString('policy', policyPretty)
outString += " Correct solution:\n %s\n" % self.prettyValueSolutionString('policy', solutionDict['policy'])
stdOutString += outString
fileOutString += outString
return testPass, stdOutString, fileOutString
def writeSolution(self, moduleDict, filePath):
with open(filePath, 'w') as handle:
policyPretty = ''
actions = []
for n in self.numsIterationsForDisplay:
valuesPretty, qValuesPretty, actions, policyPretty = self.runAgent(moduleDict, n)
handle.write(self.prettyValueSolutionString('values_k_%d' % n, valuesPretty))
for action in actions:
handle.write(self.prettyValueSolutionString('q_values_k_%d_action_%s' % (n, action), qValuesPretty[action]))
handle.write(self.prettyValueSolutionString('policy', policyPretty))
handle.write(self.prettyValueSolutionString('actions', '\n'.join(actions) + '\n'))
return True
def runAgent(self, moduleDict, numIterations):
agent = moduleDict['valueIterationAgents'].ValueIterationAgent(self.grid, discount=self.discount, iterations=numIterations)
states = self.grid.getStates()
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.getValue(state)
policy[state] = agent.computeActionFromValues(state)
possibleActions = self.grid.getPossibleActions(state)
for action in actions:
if not qValues.has_key(action):
qValues[action] = {}
if action in possibleActions:
qValues[action][state] = agent.computeQValueFromValues(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)
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 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 = range(min(numExperiences, maxPreExperiences))
self.testOutFile = testDict['test_out_file']
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 = filter(lambda state : len(self.grid.getPossibleActions(state)) > 0, self.grid.getStates())
states.sort()
randObj = FixedRandom().random
# choose a random start state and a random possible action from that state
# get the next state and reward from the transition function
lastExperience = None
for i in range(numExperiences):
startState = randObj.choice(states)
action = randObj.choice(self.grid.getPossibleActions(startState))
(endState, reward) = self.env.getRandomNextState(startState, action, randObj=randObj)
lastExperience = (startState, action, endState, reward)
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 not qValues.has_key(action):
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 = range(min(numExperiences, maxPreExperiences))
self.testOutFile = testDict['test_out_file']
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
if lastExperience is not None:
fileOutString += "Agent observed the transition (startState = %s, action = %s, endState = %s, reward = %f)\n\n\n" % lastExperience
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
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 = filter(lambda state : len(self.grid.getPossibleActions(state)) > 0, self.grid.getStates())
states.sort()
randObj = FixedRandom().random
# choose a random start state and a random possible action from that state
# get the next state and reward from the transition function
lastExperience = None
for i in range(numExperiences):
startState = randObj.choice(states)
action = randObj.choice(self.grid.getPossibleActions(startState))
(endState, reward) = self.env.getRandomNextState(startState, action, randObj=randObj)
lastExperience = (startState, action, endState, reward)
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 not qValues.has_key(action):
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
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}
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 = filter(lambda state : len(self.grid.getPossibleActions(state)) > 0, self.grid.getStates())
states.sort()
randObj = FixedRandom().random
# choose a random start state and a random possible action from that state
# get the next state and reward from the transition function
for i in range(self.numExperiences):
startState = randObj.choice(states)
action = randObj.choice(self.grid.getPossibleActions(startState))
(endState, reward) = self.env.getRandomNextState(startState, action, randObj=randObj)
agent.update(startState, action, endState, reward)
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
### q6
class Question6Test(testClasses.TestCase):
def __init__(self, question, testDict):
super(Question6Test, self).__init__(question, testDict)
def execute(self, grades, moduleDict, solutionDict):
studentSolution = moduleDict['analysis'].question6()
studentSolution = str(studentSolution).strip().lower()
hashedSolution = sha1(studentSolution).hexdigest()
if hashedSolution == '46729c96bb1e4081fdc81a8ff74b3e5db8fba415':
return self.testPass(grades)
else:
self.addMessage("Solution is not correct.")
self.addMessage(" Student solution: %s" % (studentSolution,))
return self.testFail(grades)
def writeSolution(self, moduleDict, filePath):
handle = open(filePath, 'w')
handle.write('# This is the solution file for %s.\n' % self.path)
handle.write('# File intentionally blank.\n')
handle.close()
return True
### q7/q8
### =====
## Average wins of a pacman agent
class EvalAgentTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(EvalAgentTest, self).__init__(question, testDict)
self.pacmanParams = testDict['pacmanParams']
self.scoreMinimum = int(testDict['scoreMinimum']) if 'scoreMinimum' in testDict else None
self.nonTimeoutMinimum = int(testDict['nonTimeoutMinimum']) if 'nonTimeoutMinimum' in testDict else None
self.winsMinimum = int(testDict['winsMinimum']) if 'winsMinimum' in testDict else None
self.scoreThresholds = [int(s) for s in testDict.get('scoreThresholds','').split()]
self.nonTimeoutThresholds = [int(s) for s in testDict.get('nonTimeoutThresholds','').split()]
self.winsThresholds = [int(s) for s in testDict.get('winsThresholds','').split()]
self.maxPoints = sum([len(t) for t in [self.scoreThresholds, self.nonTimeoutThresholds, self.winsThresholds]])
def execute(self, grades, moduleDict, solutionDict):
self.addMessage('Grading agent using command: python pacman.py %s'% (self.pacmanParams,))
startTime = time.time()
games = pacman.runGames(** pacman.readCommand(self.pacmanParams.split(' ')))
totalTime = time.time() - startTime
numGames = len(games)
stats = {'time': totalTime, 'wins': [g.state.isWin() for g in games].count(True),
'games': games, 'scores': [g.state.getScore() for g in games],
'timeouts': [g.agentTimeout for g in games].count(True), 'crashes': [g.agentCrashed for g in games].count(True)}
averageScore = sum(stats['scores']) / float(len(stats['scores']))
nonTimeouts = numGames - stats['timeouts']
wins = stats['wins']
def gradeThreshold(value, minimum, thresholds, name):
points = 0
passed = (minimum == None) or (value >= minimum)
if passed:
for t in thresholds:
if value >= t:
points += 1
return (passed, points, value, minimum, thresholds, name)
results = [gradeThreshold(averageScore, self.scoreMinimum, self.scoreThresholds, "average score"),
gradeThreshold(nonTimeouts, self.nonTimeoutMinimum, self.nonTimeoutThresholds, "games not timed out"),
gradeThreshold(wins, self.winsMinimum, self.winsThresholds, "wins")]
totalPoints = 0
for passed, points, value, minimum, thresholds, name in results:
if minimum == None and len(thresholds)==0:
continue
# print passed, points, value, minimum, thresholds, name
totalPoints += points
if not passed:
assert points == 0
self.addMessage("%s %s (fail: below minimum value %s)" % (value, name, minimum))
else:
self.addMessage("%s %s (%s of %s points)" % (value, name, points, len(thresholds)))
if minimum != None:
self.addMessage(" Grading scheme:")
self.addMessage(" < %s: fail" % (minimum,))
if len(thresholds)==0 or minimum != thresholds[0]:
self.addMessage(" >= %s: 0 points" % (minimum,))
for idx, threshold in enumerate(thresholds):
self.addMessage(" >= %s: %s points" % (threshold, idx+1))
elif len(thresholds) > 0:
self.addMessage(" Grading scheme:")
self.addMessage(" < %s: 0 points" % (thresholds[0],))
for idx, threshold in enumerate(thresholds):
self.addMessage(" >= %s: %s points" % (threshold, idx+1))
if any([not passed for passed, _, _, _, _, _ in results]):
totalPoints = 0
return self.testPartial(grades, totalPoints, self.maxPoints)
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
### q2/q3
### =====
## For each parameter setting, compute the optimal policy, see if it satisfies some properties
def followPath(policy, start, numSteps=100):
state = start
path = []
for i in range(numSteps):
if state not in policy:
break
action = policy[state]
path.append("(%s,%s)" % state)
if action == 'north': nextState = state[0],state[1]+1
if action == 'south': nextState = state[0],state[1]-1
if action == 'east': nextState = state[0]+1,state[1]
if action == 'west': nextState = state[0]-1,state[1]
if action == 'exit' or action == None:
path.append('TERMINAL_STATE')
break
state = nextState
return path
def parseGrid(string):
grid = [[entry.strip() for entry in line.split()] for line in string.split('\n')]
for row in grid:
for x, col in enumerate(row):
try:
col = int(col)
except:
pass
if col == "_":
col = ' '
row[x] = col
return gridworld.makeGrid(grid)
def computePolicy(moduleDict, grid, discount):
valueIterator = moduleDict['valueIterationAgents'].ValueIterationAgent(grid, discount=discount)
policy = {}
for state in grid.getStates():
policy[state] = valueIterator.computeActionFromValues(state)
return policy
class GridPolicyTest(testClasses.TestCase):
def __init__(self, question, testDict):
super(GridPolicyTest, self).__init__(question, testDict)
# Function in module in analysis that returns (discount, noise)
self.parameterFn = testDict['parameterFn']
self.question2 = testDict.get('question2', 'false').lower() == 'true'
# GridWorld specification
# _ is empty space
# numbers are terminal states with that value
# # is a wall
# S is a start state
#
self.gridText = testDict['grid']
self.grid = gridworld.Gridworld(parseGrid(testDict['grid']))
self.gridName = testDict['gridName']
# Policy specification
# _ policy choice not checked
# N, E, S, W policy action must be north, east, south, west
#
self.policy = parseGrid(testDict['policy'])
# State the most probable path must visit
# (x,y) for a particular location; (0,0) is bottom left
# terminal for the terminal state
self.pathVisits = testDict.get('pathVisits', None)
# State the most probable path must not visit
# (x,y) for a particular location; (0,0) is bottom left
# terminal for the terminal state
self.pathNotVisits = testDict.get('pathNotVisits', None)
def execute(self, grades, moduleDict, solutionDict):
if not hasattr(moduleDict['analysis'], self.parameterFn):
self.addMessage('Method not implemented: analysis.%s' % (self.parameterFn,))
return self.testFail(grades)
result = getattr(moduleDict['analysis'], self.parameterFn)()
if type(result) == str and result.lower()[0:3] == "not":
self.addMessage('Actually, it is possible!')
return self.testFail(grades)
if self.question2:
livingReward = None
try:
discount, noise = result
discount = float(discount)
noise = float(noise)
except:
self.addMessage('Did not return a (discount, noise) pair; instead analysis.%s returned: %s' % (self.parameterFn, result))
return self.testFail(grades)
if discount != 0.9 and noise != 0.2:
self.addMessage('Must change either the discount or the noise, not both. Returned (discount, noise) = %s' % (result,))
return self.testFail(grades)
else:
try:
discount, noise, livingReward = result
discount = float(discount)
noise = float(noise)
livingReward = float(livingReward)
except:
self.addMessage('Did not return a (discount, noise, living reward) triple; instead analysis.%s returned: %s' % (self.parameterFn, result))
return self.testFail(grades)
self.grid.setNoise(noise)
if livingReward != None:
self.grid.setLivingReward(livingReward)
start = self.grid.getStartState()
policy = computePolicy(moduleDict, self.grid, discount)
## check policy
actionMap = {'N': 'north', 'E': 'east', 'S': 'south', 'W': 'west', 'X': 'exit'}
width, height = self.policy.width, self.policy.height
policyPassed = True
for x in range(width):
for y in range(height):
if self.policy[x][y] in actionMap and policy[(x,y)] != actionMap[self.policy[x][y]]:
differPoint = (x,y)
policyPassed = False
if not policyPassed:
self.addMessage('Policy not correct.')
self.addMessage(' Student policy at %s: %s' % (differPoint, policy[differPoint]))
self.addMessage(' Correct policy at %s: %s' % (differPoint, actionMap[self.policy[differPoint[0]][differPoint[1]]]))
self.addMessage(' Student policy:')
self.printPolicy(policy, False)
self.addMessage(" Legend: N,S,E,W at states which move north etc, X at states which exit,")
self.addMessage(" . at states where the policy is not defined (e.g. walls)")
self.addMessage(' Correct policy specification:')
self.printPolicy(self.policy, True)
self.addMessage(" Legend: N,S,E,W for states in which the student policy must move north etc,")
self.addMessage(" _ for states where it doesn't matter what the student policy does.")
self.printGridworld()
return self.testFail(grades)
## check path
path = followPath(policy, self.grid.getStartState())
if self.pathVisits != None and self.pathVisits not in path:
self.addMessage('Policy does not visit state %s when moving without noise.' % (self.pathVisits,))
self.addMessage(' States visited: %s' % (path,))
self.addMessage(' Student policy:')
self.printPolicy(policy, False)
self.addMessage(" Legend: N,S,E,W at states which move north etc, X at states which exit,")
self.addMessage(" . at states where policy not defined")
self.printGridworld()
return self.testFail(grades)
if self.pathNotVisits != None and self.pathNotVisits in path:
self.addMessage('Policy visits state %s when moving without noise.' % (self.pathNotVisits,))
self.addMessage(' States visited: %s' % (path,))
self.addMessage(' Student policy:')
self.printPolicy(policy, False)
self.addMessage(" Legend: N,S,E,W at states which move north etc, X at states which exit,")
self.addMessage(" . at states where policy not defined")
self.printGridworld()
return self.testFail(grades)
return self.testPass(grades)
def printGridworld(self):
self.addMessage(' Gridworld:')
for line in self.gridText.split('\n'):
self.addMessage(' ' + line)
self.addMessage(' Legend: # wall, _ empty, S start, numbers terminal states with that reward.')
def printPolicy(self, policy, policyTypeIsGrid):
if policyTypeIsGrid:
legend = {'N': 'N', 'E': 'E', 'S': 'S', 'W': 'W', ' ': '_'}
else:
legend = {'north': 'N', 'east': 'E', 'south': 'S', 'west': 'W', 'exit': 'X', '.': '.', ' ': '_'}
for ybar in range(self.grid.grid.height):
y = self.grid.grid.height-1-ybar
if policyTypeIsGrid:
self.addMessage(" %s" % (" ".join([legend[policy[x][y]] for x in range(self.grid.grid.width)]),))
else:
self.addMessage(" %s" % (" ".join([legend[policy.get((x,y), '.')] for x in range(self.grid.grid.width)]),))
# for state in sorted(self.grid.getStates()):
# if state != 'TERMINAL_STATE':
# self.addMessage(' (%s,%s) %s' % (state[0], state[1], policy[state]))
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