def main():
# manipulated variables
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [3]
manipulatedVariables['numSheep'] = [4]
manipulatedVariables['wolfType'] = ['individualReward', 'sharedReward']
manipulatedVariables['sheepConcern'] = ['selfSheep']
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
parametersAllCondtion = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', '..', 'data', 'evaluateHierarchyPlanningEnvMADDPG',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePath = lambda trajectoryFixedParameters: GetSavePath(trajectoryDirectory, trajectoryExtension, trajectoryFixedParameters)
saveTrajectoryByParameters = lambda trajectories, trajectoryFixedParameters, parameters: saveToPickle(trajectories, getTrajectorySavePath(trajectoryFixedParameters)(parameters))
numTrajectories = 200
sampleTrajectoriesForConditions = SampleTrjactoriesForConditions(numTrajectories, saveTrajectoryByParameters)
[sampleTrajectoriesForConditions(para) for para in parametersAllCondtion]
def main():
# manipulated variables
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [3]
manipulatedVariables['numSheep'] = [2] # [1, 2, 4]
manipulatedVariables['wolfType'] = ['sharedAgencyByIndividualRewardWolf']
manipulatedVariables['perturbedWolfID'] = [0]
manipulatedVariables['perturbedWolfGoalID'] = [0, 1]
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
parametersAllCondtion = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', '..', 'data', 'evaluateGoalPerturbationHighLevel',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePath = lambda trajectoryFixedParameters: GetSavePath(trajectoryDirectory, trajectoryExtension,
trajectoryFixedParameters)
saveTrajectoryByParameters = lambda trajectories, trajectoryFixedParameters, parameters: saveToPickle(trajectories,
getTrajectorySavePath(
trajectoryFixedParameters)(
parameters))
numTrajectories = 20
sampleTrajectoriesForConditions = SampleTrjactoriesForConditions(numTrajectories, saveTrajectoryByParameters)
[sampleTrajectoriesForConditions(para) for para in parametersAllCondtion]
def main():
# manipulated variables
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [2] # temp just 2
manipulatedVariables['numSheep'] = [1] # temp just 1
manipulatedVariables['inferenceSoft'] = [0.05, 0.1]
manipulatedVariables['wolfPolicySoft'] = [1.5, 2.0]
manipulatedVariables['otherCompeteRate'] = [
1.0
] # 0 never compete, 1 always compete
manipulatedVariables['competeDetectionRate'] = [
0.5
] # 0 never detect compete, 1 only detect compete
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
productedValues = it.product(
*[[(key, value) for value in values]
for key, values in manipulatedVariables.items()])
parametersAllCondtion = [
dict(list(specificValueParameter))
for specificValueParameter in productedValues
]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', '..', 'data',
'evaluateCompeteDetection',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePath = lambda trajectoryFixedParameters: GetSavePath(
trajectoryDirectory, trajectoryExtension, trajectoryFixedParameters)
saveTrajectoryByParameters = lambda trajectories, trajectoryFixedParameters, parameters: saveToPickle(
trajectories,
getTrajectorySavePath(trajectoryFixedParameters)(parameters))
numTrajectories = 200
sampleTrajectoriesForConditions = SampleTrjactoriesForConditions(
numTrajectories, saveTrajectoryByParameters)
[sampleTrajectoriesForConditions(para) for para in parametersAllCondtion]
def main():
# manipulated variables
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [2, 3]
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
productedValues = it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])
parametersAllCondtion = [dict(list(specificValueParameter)) for specificValueParameter in productedValues]
DIRNAME = os.path.dirname(__file__)
trajectoryDirectory = os.path.join(DIRNAME, '..', '..', 'data', 'diagnoseAnalysis',
'trajectories')
if not os.path.exists(trajectoryDirectory):
os.makedirs(trajectoryDirectory)
trajectoryExtension = '.pickle'
getTrajectorySavePath = lambda trajectoryFixedParameters: GetSavePath(trajectoryDirectory, trajectoryExtension, trajectoryFixedParameters)
saveTrajectoryByParameters = lambda trajectories, trajectoryFixedParameters, parameters: saveToPickle(trajectories, getTrajectorySavePath(trajectoryFixedParameters)(parameters))
numTrajectories = 10000
sampleTrajectoriesForConditions = SampleTrjactoriesForConditions(numTrajectories, saveTrajectoryByParameters)
inSmallRangeRateMeans = [sampleTrajectoriesForConditions(para) for para in parametersAllCondtion]
toSplitFrame['inSmallRangeRate'] = inSmallRangeRateMeans
toSplitFrame.plot.bar(y = 'inSmallRangeRate', ylim = (0, 1))
plt.show()
plt.suptitle('Is hierarchical planned action in small range?')
def main():
load = 1
manipulatedVariables = OrderedDict()
manipulatedVariables['numWolves'] = [3]
manipulatedVariables['numSheep'] = [1, 2, 4]
manipulatedVariables['sheepConcern'] = ['selfSheep']
manipulatedVariables['wolfType'] = ['sharedAgencyByIndividualRewardWolf']
manipulatedVariables['perturbAgentID'] = [0, 1, 2]
manipulatedVariables['perturbQuantile'] = [0.3, 0.4, 0.5, 0.6, 0.7]
manipulatedVariables['perturbDim'] = [0, 1]
manipulatedVariables['interestedDim'] = [0, 1]
manipulatedVariables['interestedAgentID'] = [0, 1, 2]
resultPath = os.path.join(dirName, '..', '..', 'evalResults')
if not os.path.exists(resultPath):
os.makedirs(resultPath)
resultLoc = os.path.join(resultPath, 'evalPerturbResult.pkl')
if not load:
levelNames = list(manipulatedVariables.keys())
levelValues = list(manipulatedVariables.values())
modelIndex = pd.MultiIndex.from_product(levelValues, names=levelNames)
toSplitFrame = pd.DataFrame(index=modelIndex)
statisticsDf = toSplitFrame.groupby(levelNames).apply(evalPerturbationPercent)
print(statisticsDf)
saveToPickle(statisticsDf, resultLoc)
else:
statisticsDf = loadFromPickle(resultLoc)
# print(statisticsDf)
toSplitFrame = statisticsDf.groupby(['numWolves', 'numSheep', 'sheepConcern', 'wolfType', 'perturbAgentID', 'perturbQuantile',
'interestedAgentID']).apply(np.mean)
resultDF = toSplitFrame[toSplitFrame.index.get_level_values('perturbAgentID') != toSplitFrame.index.get_level_values('interestedAgentID')]
print(resultDF)
figure = plt.figure(figsize=(10, 10))
plotCounter = 1
numRows = len(manipulatedVariables['numSheep'])#
numColumns = len(manipulatedVariables['perturbAgentID'])
for key, outmostSubDf in resultDF.groupby('numSheep'):#
outmostSubDf.index = outmostSubDf.index.droplevel('numSheep')#
for keyCol, outterSubDf in outmostSubDf.groupby('perturbAgentID'):
outterSubDf.index = outterSubDf.index.droplevel('perturbAgentID')
axForDraw = figure.add_subplot(numRows, numColumns, plotCounter)
for keyRow, innerSubDf in outterSubDf.groupby('interestedAgentID'):
innerSubDf.index = innerSubDf.index.droplevel('interestedAgentID')
innerSubDf.plot.line(ax = axForDraw, y='mean', label = keyRow)
if plotCounter <= numColumns:
axForDraw.title.set_text('Perturbed Agent = ' + str(keyCol) )
if plotCounter% numColumns == 1:
axForDraw.set_ylabel('Number of Sheep = ' + str(key))
axForDraw.set_xlabel('Perturbation Quantile')
# print(innerSubDf)
plotCounter += 1
# plt.xlim([0.3, 0.7])
if key == 1:
plt.ylim([10, 25])
elif key == 2:
plt.ylim([30, 140])
else:
plt.ylim([80, 165])
plt.xticks([0, 1, 2, 3, 4], manipulatedVariables['perturbQuantile'])#[0.3, 0.4, 0.5, 0.6, 0.7]
plt.legend(title='Effect on Agent', title_fontsize = 8, prop={'size': 8})
# figure.text(x=0.03, y=0.5, s='Mean Episode Kill', ha='center', va='center', rotation=90)
# plt.suptitle('MADDPG Evaluate predatorSelfishness/ preySpeed/ actionCost')
# plt.savefig(os.path.join(resultPath, 'evalRewardWithKillProbAndDistSensitiveNoBiteRewKillInfo_killNum_allcond_regroup'))
plt.show()