def __call__(self, parameters):
print(parameters)
visualizeTraj = False
numWolves = parameters['numWolves']
numSheep = parameters['numSheep']
wolfType = parameters['wolfType']
sheepConcern = parameters['sheepConcern']
## MDP Env
# state is all multi agent state # action is all multi agent action
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numWolves + numSheep))
possibleWolvesIds = wolvesID
possibleSheepIds = sheepsID
numAgents = numWolves + numSheep
numBlocks = 5 - numWolves
blocksID = list(range(numAgents, numAgents + numBlocks))
numEntities = numAgents + numBlocks
sheepSize = 0.05
wolfSize = 0.075
blockSize = 0.2
sheepMaxSpeed = 1.3 * 1
wolfMaxSpeed = 1.0 * 1
blockMaxSpeed = None
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheep + [blockSize] * numBlocks
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed] * numSheep + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True]* numAgents + [False] * numBlocks
massList = [1.0] * numEntities
reshapeActionInTransit = lambda action: action
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID, entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList, entitiesSizeList,
getCollisionForce, getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList, massList,
entityMaxSpeedList, getVelFromAgentState, getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeActionInTransit, applyActionForce, applyEnvironForce, integrateState)
isCollision = IsCollision(getPosFromAgentState)
collisonRewardWolf = 1
punishForOutOfBoundForWolf = lambda stata: 0
rewardWolf = RewardCentralControlPunishBond(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision, punishForOutOfBoundForWolf, collisonRewardWolf)
collisonRewardSheep = -1
punishForOutOfBoundForSheep = PunishForOutOfBound()
rewardSheep = RewardCentralControlPunishBond(sheepsID, wolvesID, entitiesSizeList, getPosFromAgentState, isCollision, punishForOutOfBoundForSheep, collisonRewardSheep)
forwardOneStep = ForwardOneStep(transit, rewardWolf)
reset = ResetMultiAgentChasing(numAgents, numBlocks)
isTerminal = lambda state: False
maxRunningSteps = 101
sampleTrajectory = SampleTrajectory(maxRunningSteps, isTerminal, reset, forwardOneStep)
## MDP Policy
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [64 * (numWolves - 1), 64 * (numWolves - 1)]
# Sheep Part
# ------------ model ------------------------
if sheepConcern == 'selfSheep':
sheepConcernSelfOnly = 1
if sheepConcern == 'allSheep':
sheepConcernSelfOnly = 0
numSheepToObserveWhenSheepSameOrDiff = [numSheep, 1]
numSheepToObserve = numSheepToObserveWhenSheepSameOrDiff[sheepConcernSelfOnly]
print(numSheepToObserve)
sheepModelListOfDiffWolfReward = []
sheepType = 'mixed'
if sheepType == 'mixed':
sheepPrefixList = ['maddpgIndividWolf', 'maddpg']
else:
sheepPrefixList = [sheepType]
for sheepPrefix in sheepPrefixList:
wolvesIDForSheepObserve = list(range(numWolves))
sheepsIDForSheepObserve = list(range(numWolves, numSheepToObserve + numWolves))
blocksIDForSheepObserve = list(range(numSheepToObserve + numWolves, numSheepToObserve + numWolves + numBlocks))
observeOneAgentForSheep = lambda agentID: Observe(agentID, wolvesIDForSheepObserve, sheepsIDForSheepObserve,
blocksIDForSheepObserve, getPosFromAgentState, getVelFromAgentState)
observeSheep = lambda state: [observeOneAgentForSheep(agentID)(state) for agentID in range(numWolves + numSheepToObserve)]
obsIDsForSheep = wolvesIDForSheepObserve + sheepsIDForSheepObserve + blocksIDForSheepObserve
initObsForSheepParams = observeSheep(reset()[obsIDsForSheep])
obsShapeSheep = [initObsForSheepParams[obsID].shape[0] for obsID in range(len(initObsForSheepParams))]
buildSheepModels = BuildMADDPGModels(actionDim, numWolves + numSheepToObserve, obsShapeSheep)
sheepModelsList = [buildSheepModels(layerWidth, agentID) for agentID in range(numWolves, numWolves + numSheepToObserve)]
dirName = os.path.dirname(__file__)
maxEpisode = 60000
print(sheepPrefix)
sheepFileName = "{}wolves{}sheep{}blocks{}eps_agent".format(numWolves, numSheepToObserve, numBlocks, maxEpisode)
sheepModelPaths = [os.path.join(dirName, '..', '..', 'data', 'preTrainModel', sheepPrefix + sheepFileName + str(i) + '60000eps')
for i in range(numWolves, numWolves + numSheepToObserve)]
[restoreVariables(model, path) for model, path in zip(sheepModelsList, sheepModelPaths)]
sheepModelListOfDiffWolfReward = sheepModelListOfDiffWolfReward + sheepModelsList
# Sheep Policy Function
reshapeAction = ReshapeAction()
actOneStepOneModelSheep = ActOneStep(actByPolicyTrainNoisy)
# Sheep Generate Action
numAllSheepModels = len(sheepModelListOfDiffWolfReward)
# Wolves Part
# ------------ model ------------------------
wolvesIDForWolfObserve = list(range(numWolves))
sheepsIDForWolfObserve = list(range(numWolves, numSheep + numWolves))
blocksIDForWolfObserve = list(range(numSheep + numWolves, numSheep + numWolves + numBlocks))
observeOneAgentForWolf = lambda agentID: Observe(agentID, wolvesIDForWolfObserve, sheepsIDForWolfObserve,
blocksIDForWolfObserve, getPosFromAgentState, getVelFromAgentState)
observeWolf = lambda state: [observeOneAgentForWolf(agentID)(state) for agentID in range(numWolves + numSheep)]
obsIDsForWolf = wolvesIDForWolfObserve + sheepsIDForWolfObserve + blocksIDForWolfObserve
initObsForWolfParams = observeWolf(reset()[obsIDsForWolf])
obsShapeWolf = [initObsForWolfParams[obsID].shape[0] for obsID in range(len(initObsForWolfParams))]
buildWolfModels = BuildMADDPGModels(actionDim, numWolves + numSheep, obsShapeWolf)
layerWidthForWolf = [64 * (numWolves - 1), 64 * (numWolves - 1)]
wolfModelsList = [buildWolfModels(layerWidthForWolf, agentID) for agentID in range(numWolves)]
if wolfType == 'sharedReward':
prefix = 'maddpg'
if wolfType == 'individualReward':
prefix = 'maddpgIndividWolf'
wolfFileName = "{}wolves{}sheep{}blocks{}eps_agent".format(numWolves, numSheep, numBlocks, maxEpisode)
wolfModelPaths = [os.path.join(dirName, '..', '..', 'data', 'preTrainModel', prefix + wolfFileName + str(i) + '60000eps') for i in range(numWolves)]
print(numWolves, obsShapeWolf, wolfModelPaths)
[restoreVariables(model, path) for model, path in zip(wolfModelsList, wolfModelPaths)]
actionDimReshaped = 2
cov = [0.03 ** 2 for _ in range(actionDimReshaped)]
buildGaussian = BuildGaussianFixCov(cov)
actOneStepOneModelWolf = ActOneStep(actByPolicyTrainNoNoisy)
composeWolfPolicy = lambda wolfModel: lambda state: sampleFromContinuousSpace(buildGaussian(
tuple(reshapeAction(actOneStepOneModelWolf(wolfModel, observeWolf(state))))))
#actOneStepOneModelWolf = ActOneStep(actByPolicyTrainNoisy)
#composeWolfPolicy = lambda wolfModel: lambda state: tuple(reshapeAction(actOneStepOneModelSheep(wolfModel, observeWolf(state))))
wolvesSampleActions = [composeWolfPolicy(wolfModel) for wolfModel in wolfModelsList]
trajectories = []
for trajectoryId in range(self.numTrajectories):
sheepModelsForPolicy = [sheepModelListOfDiffWolfReward[np.random.choice(numAllSheepModels)] for sheepId in possibleSheepIds]
if sheepConcernSelfOnly:
composeSheepPolicy = lambda sheepModel : lambda state: {tuple(reshapeAction(actOneStepOneModelSheep(sheepModel, observeSheep(state)))): 1}
sheepChooseActionMethod = sampleFromDistribution
sheepSampleActions = [SampleActionOnFixedIntention(selfId, possibleWolvesIds, composeSheepPolicy(sheepModel), sheepChooseActionMethod, blocksID)
for selfId, sheepModel in zip(possibleSheepIds, sheepModelsForPolicy)]
else:
composeSheepPolicy = lambda sheepModel: lambda state: tuple(reshapeAction(actOneStepOneModelSheep(sheepModel, observeSheep(state))))
sheepSampleActions = [composeSheepPolicy(sheepModel) for sheepModel in sheepModelsForPolicy]
allIndividualSampleActions = wolvesSampleActions + sheepSampleActions
sampleAction = lambda state: [sampleIndividualAction(state) for sampleIndividualAction in allIndividualSampleActions]
trajectory = sampleTrajectory(sampleAction)
trajectories.append(trajectory)
trajectoryFixedParameters = {'maxRunningSteps': maxRunningSteps}
self.saveTrajectoryByParameters(trajectories, trajectoryFixedParameters, parameters)
print(np.mean([len(tra) for tra in trajectories]))
# visualize
if visualizeTraj:
wolfColor = np.array([0.85, 0.35, 0.35])
sheepColor = np.array([0.35, 0.85, 0.35])
blockColor = np.array([0.25, 0.25, 0.25])
entitiesColorList = [wolfColor] * numWolves + [sheepColor] * numSheep + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents, getPosFromAgentState)
trajToRender = np.concatenate(trajectories)
render(trajToRender)
def __call__(self, parameters):
print(parameters)
numWolves = parameters['numWolves']
numSheep = parameters['numSheep']
numBlocks = 2
wolfSelfish = 1.0 if parameters[
'wolfType'] == 'individualReward' else 0.0
perturbedWolfID = parameters['perturbedWolfID']
perturbedWolfGoalID = parameters['perturbedWolfGoalID']
## MDP Env
numAgents = numWolves + numSheep
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numWolves + numSheep))
blocksID = list(range(numAgents, numEntities))
sheepSize = 0.05
wolfSize = 0.075
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheep + [
blockSize
] * numBlocks
costActionRatio = 0.0
sheepSpeedMultiplier = 1.0
sheepMaxSpeed = 1.3 * sheepSpeedMultiplier
wolfMaxSpeed = 1.0
blockMaxSpeed = None
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheep + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
collisionReward = 1 # for evaluation, count # of bites
isCollision = IsCollision(getPosFromAgentState)
rewardAllWolves = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision, collisionReward, wolfSelfish)
rewardWolf = lambda state, action, nextState: np.sum(
rewardAllWolves(state, action, nextState))
reshapeActionInTransit = lambda action: action
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID,
entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList,
entitiesSizeList,
getCollisionForce,
getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList,
massList, entityMaxSpeedList,
getVelFromAgentState,
getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeActionInTransit,
applyActionForce, applyEnvironForce,
integrateState)
forwardOneStep = ForwardOneStep(transit, rewardWolf)
reset = ResetMultiAgentChasingWithSeed(numAgents, numBlocks)
isTerminal = lambda state: False
maxRunningStepsToSample = 101
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample,
isTerminal, reset, forwardOneStep)
## MDP Policy
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
maxTimeStep = 75
maxEpisode = 60000
dirName = os.path.dirname(__file__)
# ------------ sheep recover variables ------------------------
numSheepToObserve = 1
sheepModelListOfDiffWolfReward = []
sheepTypeList = [0.0, 1.0]
for sheepType in sheepTypeList:
wolvesIDForSheepObserve = list(range(numWolves))
sheepsIDForSheepObserve = list(
range(numWolves, numSheepToObserve + numWolves))
blocksIDForSheepObserve = list(
range(numSheepToObserve + numWolves,
numSheepToObserve + numWolves + numBlocks))
observeOneAgentForSheep = lambda agentID: Observe(
agentID, wolvesIDForSheepObserve, sheepsIDForSheepObserve,
blocksIDForSheepObserve, getPosFromAgentState,
getVelFromAgentState)
observeSheep = lambda state: [
observeOneAgentForSheep(agentID)(state)
for agentID in range(numWolves + numSheepToObserve)
]
obsIDsForSheep = wolvesIDForSheepObserve + sheepsIDForSheepObserve + blocksIDForSheepObserve
initObsForSheepParams = observeSheep(reset()[obsIDsForSheep])
obsShapeSheep = [
initObsForSheepParams[obsID].shape[0]
for obsID in range(len(initObsForSheepParams))
]
buildSheepModels = BuildMADDPGModels(actionDim,
numWolves + numSheepToObserve,
obsShapeSheep)
sheepModelsList = [
buildSheepModels(layerWidth, agentID)
for agentID in range(numWolves, numWolves + numSheepToObserve)
]
sheepFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheepToObserve, numBlocks, maxEpisode,
maxTimeStep, sheepSpeedMultiplier, costActionRatio, sheepType)
sheepModelPaths = [
os.path.join(dirName, '..', '..', 'data', 'preTrainModel',
sheepFileName + str(i))
for i in range(numWolves, numWolves + numSheepToObserve)
]
[
restoreVariables(model, path)
for model, path in zip(sheepModelsList, sheepModelPaths)
]
sheepModelListOfDiffWolfReward = sheepModelListOfDiffWolfReward + sheepModelsList
# # actOneStep = ActOneStep(actByPolicyTrainNoisy) #TODO
actOneStep = ActOneStep(actByPolicyTrainNoNoisy)
numAllSheepModels = len(sheepModelListOfDiffWolfReward)
# ------------ wolves recover variables ------------------------
# ------------ Recover one perturbed wolf for comparison -------
numSheepForPerturbedWolf = 1
wolvesIDForPerturbedWolf = wolvesID
sheepsIDForPerturbedWolf = [sheepsID[perturbedWolfGoalID]]
blocksIDForPerturbedWolf = list(
range(numWolves + numSheep,
numEntities)) # skip the unattended sheep id
observeOneAgentForPerturbedWolf = lambda agentID: Observe(
agentID, wolvesIDForPerturbedWolf, sheepsIDForPerturbedWolf,
blocksIDForPerturbedWolf, getPosFromAgentState,
getVelFromAgentState)
observePerturbedWolf = lambda state: [
observeOneAgentForPerturbedWolf(agentID)(state)
for agentID in wolvesIDForPerturbedWolf + sheepsIDForPerturbedWolf
]
initObsForPerturbedWolfParams = observePerturbedWolf(reset())
obsShapePerturbedWolf = [
initObsForPerturbedWolfParams[obsID].shape[0]
for obsID in range(len(initObsForPerturbedWolfParams))
]
buildPerturbedWolfModels = BuildMADDPGModels(
actionDim, numWolves + numSheepForPerturbedWolf,
obsShapePerturbedWolf)
layerWidthForWolf = [128, 128]
perturbedWolfModel = buildPerturbedWolfModels(layerWidthForWolf,
perturbedWolfID)
perturbedWolfFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheepForPerturbedWolf, numBlocks, maxEpisode,
maxTimeStep, sheepSpeedMultiplier, costActionRatio, wolfSelfish)
perturbedWolfModelPath = os.path.join(
dirName, '..', '..', 'data', 'preTrainModel',
perturbedWolfFileName + str(perturbedWolfID))
restoreVariables(perturbedWolfModel, perturbedWolfModelPath)
# ------------ Recover other wolves trained with multiple goals -------
wolvesIDForWolfObserve = wolvesID
sheepsIDForWolfObserve = sheepsID
blocksIDForWolfObserve = blocksID
observeOneAgentForWolf = lambda agentID: Observe(
agentID, wolvesIDForWolfObserve, sheepsIDForWolfObserve,
blocksIDForWolfObserve, getPosFromAgentState, getVelFromAgentState)
observeWolf = lambda state: [
observeOneAgentForWolf(agentID)(state)
for agentID in range(numWolves + numSheep)
]
obsIDsForWolf = wolvesIDForWolfObserve + sheepsIDForWolfObserve + blocksIDForWolfObserve
initObsForWolfParams = observeWolf(reset()[obsIDsForWolf])
obsShapeWolf = [
initObsForWolfParams[obsID].shape[0]
for obsID in range(len(initObsForWolfParams))
]
buildWolfModels = BuildMADDPGModels(actionDim, numWolves + numSheep,
obsShapeWolf)
layerWidthForWolf = [128, 128]
wolfModelsList = [
buildWolfModels(layerWidthForWolf, agentID)
for agentID in range(numWolves)
]
wolfFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheep, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, costActionRatio, wolfSelfish)
wolfModelPaths = [
os.path.join(dirName, '..', '..', 'data', 'preTrainModel',
wolfFileName + str(i)) for i in range(numWolves)
]
[
restoreVariables(model, path)
for model, path in zip(wolfModelsList, wolfModelPaths)
]
# ------------ compose policy ---------------------
actionDimReshaped = 2
cov = [0.00000000001**2 for _ in range(actionDimReshaped)]
buildGaussian = BuildGaussianFixCov(cov)
reshapeAction = ReshapeAction()
# unperturbed policy
composeWolfPolicy = lambda wolfModel: lambda state: sampleFromContinuousSpace(
buildGaussian(
tuple(reshapeAction(actOneStep(wolfModel, observeWolf(state))))
))
wolvesSampleActions = [
composeWolfPolicy(wolfModel) for wolfModel in wolfModelsList
]
# perturbed policy
composePerturbedWolfPolicy = lambda perturbedModel: lambda state: sampleFromContinuousSpace(
buildGaussian(
tuple(
reshapeAction(
actOneStep(perturbedModel, observePerturbedWolf(state))
))))
wolvesSampleActionsPerturbed = wolvesSampleActions.copy()
wolvesSampleActionsPerturbed[
perturbedWolfID] = composePerturbedWolfPolicy(perturbedWolfModel)
trajectories = []
for trajectoryId in range(self.numTrajectories):
sheepModelsForPolicy = [
sheepModelListOfDiffWolfReward[np.random.choice(
numAllSheepModels)] for sheepId in sheepsID
]
composeSheepPolicy = lambda sheepModel: lambda state: {
tuple(
reshapeAction(actOneStep(sheepModel, observeSheep(state)))):
1
}
sheepChooseActionMethod = sampleFromDistribution
sheepSampleActions = [
SampleActionOnFixedIntention(selfId, wolvesID,
composeSheepPolicy(sheepModel),
sheepChooseActionMethod, blocksID)
for selfId, sheepModel in zip(sheepsID, sheepModelsForPolicy)
]
allIndividualSampleActionsPerturbed = wolvesSampleActionsPerturbed + sheepSampleActions
sampleActionPerturbed = lambda state: [
sampleIndividualAction(state) for sampleIndividualAction in
allIndividualSampleActionsPerturbed
]
trajectory = sampleTrajectory(sampleActionPerturbed)
trajectories.append(trajectory)
trajectoryFixedParameters = {
'maxRunningStepsToSample': maxRunningStepsToSample
}
self.saveTrajectoryByParameters(trajectories,
trajectoryFixedParameters, parameters)
def __call__(self, parameters):
print(parameters)
valuePriorEndTime = -100
deviationFor2DAction = 1.0
rationalityBetaInInference = 1.0
numWolves = parameters['numWolves']
numSheep = parameters['numSheep']
wolfType = parameters['wolfType']
wolfSelfish = 0.0 if wolfType == 'sharedAgencyBySharedRewardWolf' else 1.0
perturbedWolfID = parameters['perturbedWolfID']
perturbedWolfGoalID = parameters['perturbedWolfGoalID']
## MDP Env
numBlocks = 2
numAgents = numWolves + numSheep
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numWolves + numSheep))
blocksID = list(range(numAgents, numEntities))
sheepSize = 0.05
wolfSize = 0.075
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheep + [blockSize] * numBlocks
costActionRatio = 0.0
sheepSpeedMultiplier = 1.0
sheepMaxSpeed = 1.3 * sheepSpeedMultiplier
wolfMaxSpeed = 1.0
blockMaxSpeed = None
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed] * numSheep + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
collisionReward = 1 # for evaluation, count # of bites
isCollision = IsCollision(getPosFromAgentState)
rewardAllWolves = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision, collisionReward, wolfSelfish)
rewardWolf = lambda state, action, nextState: np.sum(rewardAllWolves(state, action, nextState))
reshapeActionInTransit = lambda action: action
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID, entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList, entitiesSizeList, getCollisionForce,
getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList, massList, entityMaxSpeedList,
getVelFromAgentState, getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeActionInTransit, applyActionForce, applyEnvironForce,
integrateState)
forwardOneStep = ForwardOneStep(transit, rewardWolf)
reset = ResetMultiAgentChasingWithSeed(numAgents, numBlocks)
isTerminal = lambda state: False
maxRunningStepsToSample = 101
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, isTerminal, reset, forwardOneStep)
## MDP Policy
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
maxTimeStep = 75
maxEpisode = 60000
dirName = os.path.dirname(__file__)
# ------------ sheep recover variables ------------------------
numSheepToObserve = 1
sheepModelListOfDiffWolfReward = []
sheepTypeList = [0.0, 1.0]
for sheepType in sheepTypeList:
wolvesIDForSheepObserve = list(range(numWolves))
sheepsIDForSheepObserve = list(range(numWolves, numSheepToObserve + numWolves))
blocksIDForSheepObserve = list(
range(numSheepToObserve + numWolves, numSheepToObserve + numWolves + numBlocks))
observeOneAgentForSheep = lambda agentID: Observe(agentID, wolvesIDForSheepObserve, sheepsIDForSheepObserve,
blocksIDForSheepObserve, getPosFromAgentState,
getVelFromAgentState)
observeSheep = lambda state: [observeOneAgentForSheep(agentID)(state) for agentID in
range(numWolves + numSheepToObserve)]
obsIDsForSheep = wolvesIDForSheepObserve + sheepsIDForSheepObserve + blocksIDForSheepObserve
initObsForSheepParams = observeSheep(reset()[obsIDsForSheep])
obsShapeSheep = [initObsForSheepParams[obsID].shape[0] for obsID in range(len(initObsForSheepParams))]
buildSheepModels = BuildMADDPGModels(actionDim, numWolves + numSheepToObserve, obsShapeSheep)
sheepModelsList = [buildSheepModels(layerWidth, agentID) for agentID in
range(numWolves, numWolves + numSheepToObserve)]
sheepFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheepToObserve, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, costActionRatio,
sheepType)
sheepModelPaths = [os.path.join(dirName, '..', '..', 'data', 'preTrainModel', sheepFileName + str(i)) for i
in range(numWolves, numWolves + numSheepToObserve)]
[restoreVariables(model, path) for model, path in zip(sheepModelsList, sheepModelPaths)]
sheepModelListOfDiffWolfReward = sheepModelListOfDiffWolfReward + sheepModelsList
actOneStep = ActOneStep(actByPolicyTrainNoNoisy)
numAllSheepModels = len(sheepModelListOfDiffWolfReward)
# ------------ recover variables for "we" ------------------------
numAgentsInWe = numWolves
numSheepInWe = 1
numBlocksForWe = numBlocks
wolvesIDForWolfObserve = list(range(numAgentsInWe))
sheepsIDForWolfObserve = list(range(numAgentsInWe, numSheepInWe + numAgentsInWe))
blocksIDForWolfObserve = list(
range(numSheepInWe + numAgentsInWe, numSheepInWe + numAgentsInWe + numBlocksForWe))
observeOneAgentForWolf = lambda agentID: Observe(agentID, wolvesIDForWolfObserve, sheepsIDForWolfObserve,
blocksIDForWolfObserve, getPosFromAgentState,
getVelFromAgentState)
observeWolf = lambda state: [observeOneAgentForWolf(agentID)(state) for agentID in
range(numAgentsInWe + numSheepInWe)]
obsIDsForWolf = wolvesIDForWolfObserve + sheepsIDForWolfObserve + blocksIDForWolfObserve
initObsForWolfParams = observeWolf(reset()[obsIDsForWolf])
obsShapeWolf = [initObsForWolfParams[obsID].shape[0] for obsID in range(len(initObsForWolfParams))]
buildWolfModels = BuildMADDPGModels(actionDim, numAgentsInWe + numSheepInWe, obsShapeWolf)
layerWidthForWolf = [128, 128]
wolfModelsList = [buildWolfModels(layerWidthForWolf, agentID) for agentID in range(numAgentsInWe)]
wolfFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheepInWe, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, costActionRatio,
wolfSelfish)
wolfModelPaths = [os.path.join(dirName, '..', '..', 'data', 'preTrainModel', wolfFileName + str(i)) for i in
range(numAgentsInWe)]
[restoreVariables(model, path) for model, path in zip(wolfModelsList, wolfModelPaths)]
# ------------ compose wolves policy no perturbation ------------------------
actionDimReshaped = 2
cov = [deviationFor2DAction ** 2 for _ in range(actionDimReshaped)] # 1
buildGaussian = BuildGaussianFixCov(cov)
actOneStep = ActOneStep(actByPolicyTrainNoNoisy)
reshapeAction = ReshapeAction()
composeCentralControlPolicy = lambda observe: ComposeCentralControlPolicyByGaussianOnDeterministicAction(
reshapeAction, observe, actOneStep, buildGaussian)
wolvesCentralControlPolicy = [composeCentralControlPolicy(observeWolf)(wolfModelsList, numAgentsInWe)] # input state, return a list of gaussian distributions with cov 1
softPolicyInInference = lambda distribution: distribution
getStateThirdPersonPerspective = lambda state, goalId, weIds: getStateOrActionThirdPersonPerspective(state,
goalId,
weIds,
blocksID) # nochange
policyForCommittedAgentsInInference = PolicyForCommittedAgent(wolvesCentralControlPolicy, softPolicyInInference,
getStateThirdPersonPerspective) # same as wolvesCentralControlPolicy(state)
concernedAgentsIds = wolvesID
calCommittedAgentsPolicyLikelihood = CalCommittedAgentsContinuousPolicyLikelihood(concernedAgentsIds,
policyForCommittedAgentsInInference,
rationalityBetaInInference)
randomActionSpace = [(5, 0), (3.5, 3.5), (0, 5), (-3.5, 3.5), (-5, 0), (-3.5, -3.5), (0, -5), (3.5, -3.5),
(0, 0)]
randomPolicy = RandomPolicy(randomActionSpace)
getStateFirstPersonPerspective = lambda state, goalId, weIds, selfId: getStateOrActionFirstPersonPerspective(
state, goalId, weIds, selfId, blocksID)
policyForUncommittedAgentsInInference = PolicyForUncommittedAgent(wolvesID, randomPolicy, softPolicyInInference,
getStateFirstPersonPerspective) # random policy, returns action distribution
calUncommittedAgentsPolicyLikelihood = CalUncommittedAgentsPolicyLikelihood(wolvesID, concernedAgentsIds,
policyForUncommittedAgentsInInference) # returns 1
# Joint Likelihood
calJointLikelihood = lambda intention, state, perceivedAction: calCommittedAgentsPolicyLikelihood(intention,
state,
perceivedAction) * \
calUncommittedAgentsPolicyLikelihood(intention,
state,
perceivedAction) # __* 1
# ------------ wolves intention ------------------------
intentionSpacesForAllWolves = [tuple(it.product(sheepsID, [tuple(wolvesID)])) for wolfId in
wolvesID] # <class 'tuple'>: ((3, (0, 1, 2)), (4, (0, 1, 2)), (5, (0, 1, 2)), (6, (0, 1, 2)))
print('intentionSpacesForAllWolves', intentionSpacesForAllWolves)
wolvesIntentionPriors = [
{tuple(intention): 1 / len(allPossibleIntentionsOneWolf) for intention in allPossibleIntentionsOneWolf} for
allPossibleIntentionsOneWolf in intentionSpacesForAllWolves]
perceptSelfAction = SampleNoisyAction(deviationFor2DAction)
perceptOtherAction = SampleNoisyAction(deviationFor2DAction)
perceptAction = PerceptImaginedWeAction(wolvesID, perceptSelfAction,
perceptOtherAction) # input self, others action
# Infer and update Intention
variablesForAllWolves = [[intentionSpace] for intentionSpace in intentionSpacesForAllWolves]
jointHypothesisSpaces = [pd.MultiIndex.from_product(variables, names=['intention']) for variables in
variablesForAllWolves]
concernedHypothesisVariable = ['intention']
priorDecayRate = 1
softPrior = SoftDistribution(priorDecayRate) # no change
inferIntentionOneStepList = [InferOneStep(jointHypothesisSpace, concernedHypothesisVariable,
calJointLikelihood, softPrior) for jointHypothesisSpace in
jointHypothesisSpaces]
if numSheep == 1:
inferIntentionOneStepList = [lambda prior, state, action: prior] * 3
adjustIntentionPriorGivenValueOfState = lambda state: 1
chooseIntention = sampleFromDistribution
updateIntentions = [UpdateIntention(intentionPrior, valuePriorEndTime, adjustIntentionPriorGivenValueOfState,
perceptAction, inferIntentionOneStep, chooseIntention)
for intentionPrior, inferIntentionOneStep in
zip(wolvesIntentionPriors, inferIntentionOneStepList)]
# reset intention and adjust intention prior attributes tools for multiple trajectory
intentionResetAttributes = ['timeStep', 'lastState', 'lastAction', 'intentionPrior', 'formerIntentionPriors']
intentionResetAttributeValues = [
dict(zip(intentionResetAttributes, [0, None, None, intentionPrior, [intentionPrior]]))
for intentionPrior in wolvesIntentionPriors]
resetIntentions = ResetObjects(intentionResetAttributeValues, updateIntentions)
returnAttributes = ['formerIntentionPriors']
getIntentionDistributions = GetObjectsValuesOfAttributes(returnAttributes, updateIntentions[1:])
attributesToRecord = ['lastAction']
recordActionForUpdateIntention = RecordValuesForObjects(attributesToRecord, updateIntentions)
# Wovels Generate Action #TODO
covForPlanning = [0.00000001 for _ in range(actionDimReshaped)]
# covForPlanning = [0.03 ** 2 for _ in range(actionDimReshaped)]
buildGaussianForPlanning = BuildGaussianFixCov(covForPlanning)
composeCentralControlPolicyForPlanning = lambda \
observe: ComposeCentralControlPolicyByGaussianOnDeterministicAction(reshapeAction,
observe, actOneStep,
buildGaussianForPlanning)
wolvesCentralControlPoliciesForPlanning = [
composeCentralControlPolicyForPlanning(observeWolf)(wolfModelsList, numAgentsInWe)]
centralControlPolicyListBasedOnNumAgentsInWeForPlanning = wolvesCentralControlPoliciesForPlanning # 0 for two agents in We, 1 for three agents...
softPolicyInPlanning = lambda distribution: distribution
policyForCommittedAgentInPlanning = PolicyForCommittedAgent(
centralControlPolicyListBasedOnNumAgentsInWeForPlanning, softPolicyInPlanning,
getStateThirdPersonPerspective)
policyForUncommittedAgentInPlanning = PolicyForUncommittedAgent(wolvesID, randomPolicy, softPolicyInPlanning,
getStateFirstPersonPerspective)
def wolfChooseActionMethod(individualContinuousDistributions):
centralControlAction = tuple(
[tuple(sampleFromContinuousSpace(distribution)) for distribution in individualContinuousDistributions])
return centralControlAction
getSelfActionIDInThirdPersonPerspective = lambda weIds, selfId: list(weIds).index(selfId)
chooseCommittedAction = GetActionFromJointActionDistribution(wolfChooseActionMethod,
getSelfActionIDInThirdPersonPerspective)
chooseUncommittedAction = sampleFromDistribution
wolvesSampleIndividualActionGivenIntentionList = [
SampleIndividualActionGivenIntention(selfId, policyForCommittedAgentInPlanning,
policyForUncommittedAgentInPlanning, chooseCommittedAction,
chooseUncommittedAction)
for selfId in wolvesID]
# ------------------- recover one wolf model that only concerns sheep 0 -------------------
numSheepForPerturbedWolf = 1
wolvesIDForPerturbedWolf = wolvesID
sheepsIDForPerturbedWolf = [sheepsID[perturbedWolfGoalID]]
blocksIDForPerturbedWolf = list(range(numWolves + numSheep, numEntities)) # skip the unattended sheep id
observeOneAgentForPerturbedWolf = lambda agentID: Observe(agentID, wolvesIDForPerturbedWolf, sheepsIDForPerturbedWolf,
blocksIDForPerturbedWolf, getPosFromAgentState, getVelFromAgentState)
observePerturbedWolf = lambda state: [observeOneAgentForPerturbedWolf(agentID)(state) for agentID in wolvesIDForPerturbedWolf + sheepsIDForPerturbedWolf]
initObsForPerturbedWolfParams = observePerturbedWolf(reset())
obsShapePerturbedWolf = [initObsForPerturbedWolfParams[obsID].shape[0] for obsID in range(len(initObsForPerturbedWolfParams))]
buildPerturbedWolfModels = BuildMADDPGModels(actionDim, numWolves + numSheepForPerturbedWolf, obsShapePerturbedWolf)
layerWidthForWolf = [128, 128]
perturbedWolfModel = buildPerturbedWolfModels(layerWidthForWolf, perturbedWolfID)
perturbedWolfFileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}WolfActCost{}individ{}_agent".format(
numWolves, numSheepForPerturbedWolf, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, costActionRatio, wolfSelfish)
perturbedWolfModelPath = os.path.join(dirName, '..', '..', 'data', 'preTrainModel', perturbedWolfFileName + str(perturbedWolfID))
restoreVariables(perturbedWolfModel, perturbedWolfModelPath)
# ------------------- Sample and Save Trajectory -------------------
wolvesSampleActions = [
SampleActionOnChangableIntention(updateIntention, wolvesSampleIndividualActionGivenIntention)
for updateIntention, wolvesSampleIndividualActionGivenIntention in
zip(updateIntentions, wolvesSampleIndividualActionGivenIntentionList)]
perturbedWolfSampleActions = lambda state: tuple(reshapeAction(actOneStep(perturbedWolfModel, observePerturbedWolf(state))))
wolvesSampleActionsPerturbed = wolvesSampleActions#.copy()
wolvesSampleActionsPerturbed[perturbedWolfID] = perturbedWolfSampleActions
trajectoriesWithIntentionDists = []
for trajectoryId in range(self.numTrajectories):
sheepModelsForPolicy = [sheepModelListOfDiffWolfReward[np.random.choice(numAllSheepModels)] for sheepId in
sheepsID]
composeSheepPolicy = lambda sheepModel: lambda state: {
tuple(reshapeAction(actOneStep(sheepModel, observeSheep(state)))): 1}
sheepChooseActionMethod = sampleFromDistribution
sheepSampleActions = [SampleActionOnFixedIntention(selfId, wolvesID, composeSheepPolicy(sheepModel),
sheepChooseActionMethod, blocksID)
for selfId, sheepModel in zip(sheepsID, sheepModelsForPolicy)]
allIndividualSampleActions = wolvesSampleActions + sheepSampleActions
sampleActionMultiAgent = SampleActionMultiagent(allIndividualSampleActions, recordActionForUpdateIntention)
allIndividualSampleActionsPerturbed = wolvesSampleActionsPerturbed + sheepSampleActions
sampleActionMultiAgentPerturbed = SampleActionMultiagent(allIndividualSampleActionsPerturbed, recordActionForUpdateIntention)
# trajectory = sampleTrajectory(sampleActionMultiAgentPerturbed)
trajectory = sampleTrajectory(sampleActionMultiAgentPerturbed)
intentionDistributions = getIntentionDistributions()
trajectoryWithIntentionDists = [tuple(list(SASRPair) + list(intentionDist)) for SASRPair, intentionDist in
zip(trajectory, intentionDistributions)]
trajectoriesWithIntentionDists.append(tuple(trajectoryWithIntentionDists))
# trajectoriesWithIntentionDists.append(trajectory)
resetIntentions()
trajectoryFixedParameters = {'maxRunningStepsToSample': maxRunningStepsToSample}
self.saveTrajectoryByParameters(trajectoriesWithIntentionDists, trajectoryFixedParameters, parameters)
def __call__(self, parameters):
print(parameters)
visualizeTraj = False
numWolves = parameters['numWolves']
numSheep = parameters['numSheep']
softParamterForValue = parameters['valuePriorSoftMaxBeta']
valuePriorEndTime = parameters['valuePriorEndTime']
deviationFor2DAction = parameters['deviationFor2DAction']
rationalityBetaInInference = parameters['rationalityBetaInInference']
wolfType = parameters['wolfType']
sheepConcern = parameters['sheepConcern']
print(rationalityBetaInInference)
## MDP Env
# state is all multi agent state # action is all multi agent action
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numWolves + numSheep))
possibleWolvesIds = wolvesID
possibleSheepIds = sheepsID
numAgents = numWolves + numSheep
numBlocks = 5 - numWolves
blocksID = list(range(numAgents, numAgents + numBlocks))
numEntities = numAgents + numBlocks
sheepSize = 0.05
wolfSize = 0.075
blockSize = 0.2
sheepMaxSpeed = 1.3 * 1
wolfMaxSpeed = 1.0 * 1
blockMaxSpeed = None
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheep + [
blockSize
] * numBlocks
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheep + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
reshapeActionInTransit = lambda action: action
getCollisionForce = GetCollisionForce()
applyActionForce = ApplyActionForce(wolvesID, sheepsID,
entitiesMovableList)
applyEnvironForce = ApplyEnvironForce(numEntities, entitiesMovableList,
entitiesSizeList,
getCollisionForce,
getPosFromAgentState)
integrateState = IntegrateState(numEntities, entitiesMovableList,
massList, entityMaxSpeedList,
getVelFromAgentState,
getPosFromAgentState)
transit = TransitMultiAgentChasing(numEntities, reshapeActionInTransit,
applyActionForce, applyEnvironForce,
integrateState)
isCollision = IsCollision(getPosFromAgentState)
collisonRewardWolf = 1
punishForOutOfBoundForWolf = lambda stata: 0
rewardWolf = RewardCentralControlPunishBond(
wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState,
isCollision, punishForOutOfBoundForWolf, collisonRewardWolf)
collisonRewardSheep = -1
punishForOutOfBoundForSheep = PunishForOutOfBound()
rewardSheep = RewardCentralControlPunishBond(
sheepsID, wolvesID, entitiesSizeList, getPosFromAgentState,
isCollision, punishForOutOfBoundForSheep, collisonRewardSheep)
forwardOneStep = ForwardOneStep(transit, rewardWolf)
reset = ResetMultiAgentChasing(numAgents, numBlocks)
isTerminal = lambda state: False
maxRunningSteps = 101
sampleTrajectory = SampleTrajectory(maxRunningSteps, isTerminal, reset,
forwardOneStep)
## MDP Policy
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [64 * (numWolves - 1), 64 * (numWolves - 1)]
# Sheep Part
# ------------ model ------------------------
if sheepConcern == 'selfSheep':
sheepConcernSelfOnly = 1
if sheepConcern == 'allSheep':
sheepConcernSelfOnly = 0
numSheepToObserveWhenSheepSameOrDiff = [numSheep, 1]
numSheepToObserve = numSheepToObserveWhenSheepSameOrDiff[
sheepConcernSelfOnly]
print(numSheepToObserve)
sheepModelListOfDiffWolfReward = []
sheepType = 'mixed'
if sheepType == 'mixed':
sheepPrefixList = ['maddpgIndividWolf', 'maddpg']
else:
sheepPrefixList = [sheepType]
for sheepPrefix in sheepPrefixList:
wolvesIDForSheepObserve = list(range(numWolves))
sheepsIDForSheepObserve = list(
range(numWolves, numSheepToObserve + numWolves))
blocksIDForSheepObserve = list(
range(numSheepToObserve + numWolves,
numSheepToObserve + numWolves + numBlocks))
observeOneAgentForSheep = lambda agentID: Observe(
agentID, wolvesIDForSheepObserve, sheepsIDForSheepObserve,
blocksIDForSheepObserve, getPosFromAgentState,
getVelFromAgentState)
observeSheep = lambda state: [
observeOneAgentForSheep(agentID)(state)
for agentID in range(numWolves + numSheepToObserve)
]
obsIDsForSheep = wolvesIDForSheepObserve + sheepsIDForSheepObserve + blocksIDForSheepObserve
initObsForSheepParams = observeSheep(reset()[obsIDsForSheep])
obsShapeSheep = [
initObsForSheepParams[obsID].shape[0]
for obsID in range(len(initObsForSheepParams))
]
buildSheepModels = BuildMADDPGModels(actionDim,
numWolves + numSheepToObserve,
obsShapeSheep)
sheepModelsList = [
buildSheepModels(layerWidth, agentID)
for agentID in range(numWolves, numWolves + numSheepToObserve)
]
dirName = os.path.dirname(__file__)
maxEpisode = 60000
print(sheepPrefix)
sheepFileName = "{}wolves{}sheep{}blocks{}eps_agent".format(
numWolves, numSheepToObserve, numBlocks, maxEpisode)
sheepModelPaths = [
os.path.join(dirName, '..', '..', 'data', 'preTrainModel',
sheepPrefix + sheepFileName + str(i) + '60000eps')
for i in range(numWolves, numWolves + numSheepToObserve)
]
[
restoreVariables(model, path)
for model, path in zip(sheepModelsList, sheepModelPaths)
]
sheepModelListOfDiffWolfReward = sheepModelListOfDiffWolfReward + sheepModelsList
# Sheep Policy Function
reshapeAction = ReshapeAction()
actOneStepOneModelSheep = ActOneStep(actByPolicyTrainNoisy)
# Sheep Generate Action
numAllSheepModels = len(sheepModelListOfDiffWolfReward)
# Wolves Part
# Intention Prior For inference
#createIntentionSpaceGivenSelfId = CreateIntentionSpaceGivenSelfId(possibleSheepIds, possibleWolvesIds)
#intentionSpacesForAllWolves = [createAllPossibleIntentionsGivenSelfId(wolfId)
# for wolfId in possibleWolvesIds]
intentionSpacesForAllWolves = [
tuple(it.product(possibleSheepIds, [tuple(possibleWolvesIds)]))
for wolfId in possibleWolvesIds
]
print(intentionSpacesForAllWolves)
wolvesIntentionPriors = [{
tuple(intention): 1 / len(allPossibleIntentionsOneWolf)
for intention in allPossibleIntentionsOneWolf
} for allPossibleIntentionsOneWolf in intentionSpacesForAllWolves]
# Percept Action For Inference
#perceptAction = lambda action: action
perceptSelfAction = SampleNoisyAction(deviationFor2DAction)
perceptOtherAction = SampleNoisyAction(deviationFor2DAction)
perceptAction = PerceptImaginedWeAction(possibleWolvesIds,
perceptSelfAction,
perceptOtherAction)
#perceptAction = lambda action: action
# Policy Likelihood function: Wolf Centrol Control NN Policy Given Intention
# ------------ model ------------------------
weModelsListBaseOnNumInWe = []
observeListBaseOnNumInWe = []
for numAgentInWe in range(2, numWolves + 1):
numBlocksForWe = 5 - numAgentInWe
wolvesIDForWolfObserve = list(range(numAgentInWe))
sheepsIDForWolfObserve = list(range(numAgentInWe,
1 + numAgentInWe))
blocksIDForWolfObserve = list(
range(1 + numAgentInWe, 1 + numAgentInWe + numBlocksForWe))
observeOneAgentForWolf = lambda agentID: Observe(
agentID, wolvesIDForWolfObserve, sheepsIDForWolfObserve,
blocksIDForWolfObserve, getPosFromAgentState,
getVelFromAgentState)
observeWolf = lambda state: [
observeOneAgentForWolf(agentID)(state)
for agentID in range(numAgentInWe + 1)
]
observeListBaseOnNumInWe.append(observeWolf)
obsIDsForWolf = wolvesIDForWolfObserve + sheepsIDForWolfObserve + blocksIDForWolfObserve
initObsForWolfParams = observeWolf(reset()[obsIDsForWolf])
obsShapeWolf = [
initObsForWolfParams[obsID].shape[0]
for obsID in range(len(initObsForWolfParams))
]
buildWolfModels = BuildMADDPGModels(actionDim, numAgentInWe + 1,
obsShapeWolf)
layerWidthForWolf = [
64 * (numAgentInWe - 1), 64 * (numAgentInWe - 1)
]
wolfModelsList = [
buildWolfModels(layerWidthForWolf, agentID)
for agentID in range(numAgentInWe)
]
if wolfType == 'sharedAgencyByIndividualRewardWolf':
wolfPrefix = 'maddpgIndividWolf'
if wolfType == 'sharedAgencyBySharedRewardWolf':
wolfPrefix = 'maddpg'
wolfFileName = "{}wolves{}sheep{}blocks{}eps_agent".format(
numAgentInWe, 1, numBlocksForWe, maxEpisode)
wolfModelPaths = [
os.path.join(dirName, '..', '..', 'data', 'preTrainModel',
wolfPrefix + wolfFileName + str(i) + '60000eps')
for i in range(numAgentInWe)
]
print(numAgentInWe, obsShapeWolf, wolfModelPaths)
[
restoreVariables(model, path)
for model, path in zip(wolfModelsList, wolfModelPaths)
]
weModelsListBaseOnNumInWe.append(wolfModelsList)
actionDimReshaped = 2
cov = [deviationFor2DAction**2 for _ in range(actionDimReshaped)]
buildGaussian = BuildGaussianFixCov(cov)
actOneStepOneModelWolf = ActOneStep(actByPolicyTrainNoNoisy)
#actOneStepOneModelWolf = ActOneStep(actByPolicyTrainNoisy)
composeCentralControlPolicy = lambda observe: ComposeCentralControlPolicyByGaussianOnDeterministicAction(
reshapeAction, observe, actOneStepOneModelWolf, buildGaussian)
wolvesCentralControlPolicies = [
composeCentralControlPolicy(
observeListBaseOnNumInWe[numAgentsInWe - 2])(
weModelsListBaseOnNumInWe[numAgentsInWe - 2],
numAgentsInWe)
for numAgentsInWe in range(2, numWolves + 1)
]
centralControlPolicyListBasedOnNumAgentsInWe = wolvesCentralControlPolicies # 0 for two agents in We, 1 for three agents...
softPolicyInInference = lambda distribution: distribution
getStateThirdPersonPerspective = lambda state, goalId, weIds: getStateOrActionThirdPersonPerspective(
state, goalId, weIds, blocksID)
policyForCommittedAgentsInInference = PolicyForCommittedAgent(
centralControlPolicyListBasedOnNumAgentsInWe,
softPolicyInInference, getStateThirdPersonPerspective)
concernedAgentsIds = possibleWolvesIds
calCommittedAgentsPolicyLikelihood = CalCommittedAgentsContinuousPolicyLikelihood(
concernedAgentsIds, policyForCommittedAgentsInInference,
rationalityBetaInInference)
randomActionSpace = [(5, 0), (3.5, 3.5), (0, 5), (-3.5, 3.5), (-5, 0),
(-3.5, -3.5), (0, -5), (3.5, -3.5), (0, 0)]
randomPolicy = RandomPolicy(randomActionSpace)
getStateFirstPersonPerspective = lambda state, goalId, weIds, selfId: getStateOrActionFirstPersonPerspective(
state, goalId, weIds, selfId, blocksID)
policyForUncommittedAgentsInInference = PolicyForUncommittedAgent(
possibleWolvesIds, randomPolicy, softPolicyInInference,
getStateFirstPersonPerspective)
calUncommittedAgentsPolicyLikelihood = CalUncommittedAgentsPolicyLikelihood(
possibleWolvesIds, concernedAgentsIds,
policyForUncommittedAgentsInInference)
# Joint Likelihood
calJointLikelihood = lambda intention, state, perceivedAction: calCommittedAgentsPolicyLikelihood(intention, state, perceivedAction) * \
calUncommittedAgentsPolicyLikelihood(intention, state, perceivedAction)
# Infer and update Intention
variablesForAllWolves = [
[intentionSpace] for intentionSpace in intentionSpacesForAllWolves
]
jointHypothesisSpaces = [
pd.MultiIndex.from_product(variables, names=['intention'])
for variables in variablesForAllWolves
]
concernedHypothesisVariable = ['intention']
priorDecayRate = 1
softPrior = SoftDistribution(priorDecayRate)
inferIntentionOneStepList = [
InferOneStep(jointHypothesisSpace, concernedHypothesisVariable,
calJointLikelihood, softPrior)
for jointHypothesisSpace in jointHypothesisSpaces
]
if numSheep == 1:
inferIntentionOneStepList = [lambda prior, state, action: prior
] * 3
adjustIntentionPriorGivenValueOfState = lambda state: 1
chooseIntention = sampleFromDistribution
updateIntentions = [
UpdateIntention(intentionPrior, valuePriorEndTime,
adjustIntentionPriorGivenValueOfState,
perceptAction, inferIntentionOneStep,
chooseIntention)
for intentionPrior, inferIntentionOneStep in zip(
wolvesIntentionPriors, inferIntentionOneStepList)
]
# reset intention and adjuste intention prior attributes tools for multiple trajectory
intentionResetAttributes = [
'timeStep', 'lastState', 'lastAction', 'intentionPrior',
'formerIntentionPriors'
]
intentionResetAttributeValues = [
dict(
zip(intentionResetAttributes,
[0, None, None, intentionPrior, [intentionPrior]]))
for intentionPrior in wolvesIntentionPriors
]
resetIntentions = ResetObjects(intentionResetAttributeValues,
updateIntentions)
returnAttributes = ['formerIntentionPriors']
getIntentionDistributions = GetObjectsValuesOfAttributes(
returnAttributes, updateIntentions)
attributesToRecord = ['lastAction']
recordActionForUpdateIntention = RecordValuesForObjects(
attributesToRecord, updateIntentions)
# Wovels Generate Action
covForPlanning = [0.03**2 for _ in range(actionDimReshaped)]
buildGaussianForPlanning = BuildGaussianFixCov(covForPlanning)
composeCentralControlPolicyForPlanning = lambda observe: ComposeCentralControlPolicyByGaussianOnDeterministicAction(
reshapeAction, observe, actOneStepOneModelWolf,
buildGaussianForPlanning)
wolvesCentralControlPoliciesForPlanning = [
composeCentralControlPolicyForPlanning(
observeListBaseOnNumInWe[numAgentsInWe - 2])(
weModelsListBaseOnNumInWe[numAgentsInWe - 2],
numAgentsInWe)
for numAgentsInWe in range(2, numWolves + 1)
]
centralControlPolicyListBasedOnNumAgentsInWeForPlanning = wolvesCentralControlPoliciesForPlanning # 0 for two agents in We, 1 for three agents...
softPolicyInPlanning = lambda distribution: distribution
policyForCommittedAgentInPlanning = PolicyForCommittedAgent(
centralControlPolicyListBasedOnNumAgentsInWeForPlanning,
softPolicyInPlanning, getStateThirdPersonPerspective)
policyForUncommittedAgentInPlanning = PolicyForUncommittedAgent(
possibleWolvesIds, randomPolicy, softPolicyInPlanning,
getStateFirstPersonPerspective)
def wolfChooseActionMethod(individualContinuousDistributions):
centralControlAction = tuple([
tuple(sampleFromContinuousSpace(distribution))
for distribution in individualContinuousDistributions
])
return centralControlAction
getSelfActionThirdPersonPerspective = lambda weIds, selfId: list(
weIds).index(selfId)
chooseCommittedAction = GetActionFromJointActionDistribution(
wolfChooseActionMethod, getSelfActionThirdPersonPerspective)
chooseUncommittedAction = sampleFromDistribution
wolvesSampleIndividualActionGivenIntentionList = [
SampleIndividualActionGivenIntention(
selfId, policyForCommittedAgentInPlanning,
policyForUncommittedAgentInPlanning, chooseCommittedAction,
chooseUncommittedAction) for selfId in possibleWolvesIds
]
# Sample and Save Trajectory
trajectoriesWithIntentionDists = []
for trajectoryId in range(self.numTrajectories):
sheepModelsForPolicy = [
sheepModelListOfDiffWolfReward[np.random.choice(
numAllSheepModels)] for sheepId in possibleSheepIds
]
if sheepConcernSelfOnly:
composeSheepPolicy = lambda sheepModel: lambda state: {
tuple(
reshapeAction(
actOneStepOneModelSheep(sheepModel,
observeSheep(state)))):
1
}
sheepChooseActionMethod = sampleFromDistribution
sheepSampleActions = [
SampleActionOnFixedIntention(
selfId, possibleWolvesIds,
composeSheepPolicy(sheepModel),
sheepChooseActionMethod, blocksID) for selfId,
sheepModel in zip(possibleSheepIds, sheepModelsForPolicy)
]
else:
composeSheepPolicy = lambda sheepModel: lambda state: tuple(
reshapeAction(
actOneStepOneModelSheep(sheepModel, observeSheep(state)
)))
sheepSampleActions = [
composeSheepPolicy(sheepModel)
for sheepModel in sheepModelsForPolicy
]
wolvesSampleActions = [
SampleActionOnChangableIntention(
updateIntention,
wolvesSampleIndividualActionGivenIntention)
for updateIntention, wolvesSampleIndividualActionGivenIntention
in zip(updateIntentions,
wolvesSampleIndividualActionGivenIntentionList)
]
allIndividualSampleActions = wolvesSampleActions + sheepSampleActions
sampleActionMultiAgent = SampleActionMultiagent(
allIndividualSampleActions, recordActionForUpdateIntention)
trajectory = sampleTrajectory(sampleActionMultiAgent)
intentionDistributions = getIntentionDistributions()
trajectoryWithIntentionDists = [
tuple(list(SASRPair) + list(intentionDist)) for SASRPair,
intentionDist in zip(trajectory, intentionDistributions)
]
trajectoriesWithIntentionDists.append(
tuple(trajectoryWithIntentionDists))
resetIntentions()
#print(intentionDistributions)
trajectoryFixedParameters = {'maxRunningSteps': maxRunningSteps}
self.saveTrajectoryByParameters(trajectoriesWithIntentionDists,
trajectoryFixedParameters, parameters)
print(np.mean([len(tra) for tra in trajectoriesWithIntentionDists]))
# visualize
if visualizeTraj:
wolfColor = np.array([0.85, 0.35, 0.35])
sheepColor = np.array([0.35, 0.85, 0.35])
blockColor = np.array([0.25, 0.25, 0.25])
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheep + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
trajToRender = np.concatenate(trajectoriesWithIntentionDists)
render(trajToRender)