def simuliateOneParameter(parameterDict, evalNum, randomSeed, dt):
mujocoVisualize = False
demoVisualize = False
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
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])
wolvesID = [0]
sheepsID = [1]
blocksID = [2]
numWolves = len(wolvesID)
numSheeps = len(sheepsID)
numBlocks = len(blocksID)
sheepMaxSpeed = 1.3
wolfMaxSpeed = 1.0
blockMaxSpeed = None
agentsMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed
] * numSheeps
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
makePropertyList = MakePropertyList(transferNumberListToStr)
#changeCollisionReleventParameter
dmin = parameterDict['dmin']
dmax = parameterDict['dmax']
width = parameterDict['width']
midpoint = parameterDict['midpoint']
power = parameterDict['power']
timeconst = parameterDict['timeconst']
dampratio = parameterDict['dampratio']
geomIds = [1, 2, 3]
keyNameList = ['@solimp', '@solref']
valueList = [[[dmin, dmax, width, midpoint, power], [timeconst, dampratio]]
] * len(geomIds)
collisionParameter = makePropertyList(geomIds, keyNameList, valueList)
#changeSize
# geomIds=[1,2]
# keyNameList=['@size']
# valueList=[[[0.075,0.075]],[[0.1,0.1]]]
# sizeParameter=makePropertyList(geomIds,keyNameList,valueList)
#load env xml and change some geoms' property
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'variousCollision_numBlocks=1_numSheeps=1_numWolves=1dt={}.xml'.format(
dt))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlPropertyDictList = [collisionParameter]
changeEnvXmlPropertFuntionyList = [changeGeomProperty]
for propertyDict, changeXmlProperty in zip(
envXmlPropertyDictList, changeEnvXmlPropertFuntionyList):
envXmlDict = changeXmlProperty(envXmlDict, propertyDict)
envXml = xmltodict.unparse(envXmlDict)
# print(envXmlDict['mujoco']['worldbody']['body'][0])
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# MDP function
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0
qPosInitNoise = 1
# reset=ResetUniformWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents, numBlocks,qPosInitNoise, qVelInitNoise)
# fixReset=ResetFixWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents,numBlocks)
# blocksState=[[0,-0.8,0,0]]
# reset=lambda :fixReset([-0.5,0.8,-0.5,0,0.5,0.8],[0,0,0,0,0,0],blocksState)
isTerminal = lambda state: False
numSimulationFrames = int(0.1 / dt)
print(numSimulationFrames)
# dt=0.01
damping = 2.5
reshapeAction = lambda action: action
# transit = TransitionFunctionWithoutXPos(physicsSimulation,numAgents , numSimulationFrames,damping*dt/numSimulationFrames,agentsMaxSpeedList,mujocoVisualize,reshapeAction)
maxRunningSteps = 10
# sampleTrajectory = SampleTrajectory(maxRunningSteps, transit, isTerminal, rewardFunc, reset)
# observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState, getVelFromAgentState)
# observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
# initObsForParams = observe(reset())
# obsShape = [initObsForParams[obsID].shape for obsID in range(len(initObsForParams))]
class ImpulsePolicy(object):
"""docstring for c"""
def __init__(self, initAction):
self.initAction = initAction
def __call__(self, state, timeStep):
action = [[0, 0], [0, 0]]
if timeStep == 0:
action = self.initAction
return action
worldDim = 2
trajList = []
startTime = time.time()
for i in range(evalNum):
np.random.seed(randomSeed + i)
initSpeedDirection = np.random.uniform(-np.pi / 2, np.pi / 2, 1)[0]
initSpeed = np.random.uniform(0, 1, 1)[0]
initActionDirection = np.random.uniform(-np.pi / 2, np.pi / 2, 1)[0]
initForce = np.random.uniform(0, 5, 1)[0]
# print(initSpeedDirection,initSpeed,initActionDirection,initForce)
fixReset = ResetFixWithoutXPos(physicsSimulation, qPosInit, qVelInit,
numAgents, numBlocks)
blocksState = [[0, 0, 0, 0]] #posX posY velX velY
reset = lambda: fixReset([-0.28, 0, 8, 8], [
initSpeed * np.cos(initSpeedDirection), initSpeed * np.sin(
initSpeedDirection), 0, 0
], blocksState)
transit = TransitionFunctionWithoutXPos(
physicsSimulation, numAgents, numSimulationFrames,
damping * dt * numSimulationFrames, agentsMaxSpeedList,
mujocoVisualize, isTerminal, reshapeAction)
initAction = [[
initForce * np.cos(initActionDirection),
initForce * np.sin(initActionDirection)
], [0, 0]]
impulsePolicy = ImpulsePolicy(initAction)
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, rewardFunc, reset)
traj = sampleTrajectory(impulsePolicy)
# print('traj',traj[0])
trajList.append(traj)
# saveTraj
# print(trajList)
saveTraj = True
if saveTraj:
# trajSavePath = os.path.join(dirName,'traj', 'evaluateCollision', 'CollisionMoveTransitDamplingCylinder.pickle')
trajectorySaveExtension = '.pickle'
fixedParameters = {
'isMujoco': 1,
'isCylinder': 1,
'randomSeed': randomSeed,
'evalNum': evalNum
}
trajectoriesSaveDirectory = trajSavePath = os.path.join(
dirName, '..', 'trajectory', 'variousCollsiondt={}'.format(dt))
generateTrajectorySavePath = GetSavePath(trajectoriesSaveDirectory,
trajectorySaveExtension,
fixedParameters)
trajectorySavePath = generateTrajectorySavePath(parameterDict)
saveToPickle(trajList, trajectorySavePath)
# visualize
if demoVisualize:
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
trajToRender = np.concatenate(trajList)
render(trajToRender)
endTime = time.time()
print("Time taken {} seconds to generate {} trajectories".format(
(endTime - startTime), evalNum))
def simuliateOneParameter(parameterDict):
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
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])
wolvesID = [0,1]
sheepsID = [2]
blocksID = [3]
numWolves = len(wolvesID)
numSheeps = len(sheepsID)
numBlocks = len(blocksID)
sheepMaxSpeed = 1.3
wolfMaxSpeed =1.0
blockMaxSpeed = None
agentsMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
entitiesSizeList = [wolfSize]* numWolves + [sheepSize] * numSheeps + [blockSize]* numBlocks
entityMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps + [blockMaxSpeed]* numBlocks
entitiesMovableList = [True]* numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision, punishForOutOfBound)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
makePropertyList=MakePropertyList(transferNumberListToStr)
#changeCollisionReleventParameter
dmin=parameterDict['dmin']
dmax=parameterDict['dmax']
width=parameterDict['width']
midpoint=parameterDict['midpoint']
power=parameterDict['power']
timeconst=parameterDict['timeconst']
dampratio=parameterDict['dampratio']
geomIds=[1,2]
keyNameList=['@solimp','@solref']
valueList=[[[dmin,dmax,width,midpoint,power],[timeconst,dampratio]]]*len(geomIds)
collisionParameter=makePropertyList(geomIds,keyNameList,valueList)
#changeSize
# geomIds=[1,2]
# keyNameList=['@size']
# valueList=[[[0.075,0.075]],[[0.1,0.1]]]
# sizeParameter=makePropertyList(geomIds,keyNameList,valueList)
#load env xml and change some geoms' property
# physicsDynamicsPath=os.path.join(dirName,'multiAgentcollision.xml')
physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','multiAgentcollision.xml')
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
print(envXmlDict)
envXmlPropertyDictList=[collisionParameter]
changeEnvXmlPropertFuntionyList=[changeGeomProperty]
for propertyDict,changeXmlProperty in zip(envXmlPropertyDictList,changeEnvXmlPropertFuntionyList):
envXmlDict=changeXmlProperty(envXmlDict,propertyDict)
envXml=xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# MDP function
qPosInit = [0, 0]*numAgents
qVelInit = [0, 0]*numAgents
qVelInitNoise = 0
qPosInitNoise = 1
# reset=ResetUniformWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents, numBlocks,qPosInitNoise, qVelInitNoise)
fixReset=ResetFixWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents,numBlocks)
blocksState=[[0,0,0,0]]
reset=lambda :fixReset([-0.5,0.8,-0.5,0,0.5,0.8],[0,0,0,0,0,0],blocksState)
isTerminal = lambda state: False
numSimulationFrames = 1
visualize=False
# physicsViewer=None
dt=0.1
damping=2.5
transit = TransitionFunctionWithoutXPos(physicsSimulation,numAgents , numSimulationFrames,damping*dt/numSimulationFrames,agentsMaxSpeedList,visualize,isTerminal)
maxRunningSteps = 100
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit, isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState, getVelFromAgentState)
observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
initObsForParams = observe(reset())
obsShape = [initObsForParams[obsID].shape for obsID in range(len(initObsForParams))]
worldDim = 2
evalNum=1
trajectorySaveExtension = '.pickle'
fixedParameters = {'isMujoco': 1,'isCylinder':1,'evalNum':evalNum}
trajectoriesSaveDirectory=trajSavePath = os.path.join(dirName,'..','trajectory')
generateTrajectorySavePath = GetSavePath(trajectoriesSaveDirectory, trajectorySaveExtension, fixedParameters)
trajectorySavePath = generateTrajectorySavePath(parameterDict)
if not os.path.isfile(trajectorySavePath):
trajList =list()
for i in range(evalNum):
# policy =lambda state: [[-3,0] for agent in range(numAgents)]
np.random.seed(i)
# policy =lambda state: [np.random.uniform(-5,5,2) for agent in range(numAgents)]sss
# policy =lambda state: [[0,1] for agent in range(numAgents)]
policy =lambda state: [[1,0] ]
# policy =lambda state: [[np.random.uniform(0,1,1),0] ,[np.random.uniform(-1,0,1),0] ]
traj = sampleTrajectory(policy)
# print(i,'traj',[state[1] for state in traj[:2]])
# print(traj)
trajList.append( traj)
# saveTraj
saveTraj = True
if saveTraj:
# trajSavePath = os.path.join(dirName,'traj', 'evaluateCollision', 'CollisionMoveTransitDamplingCylinder.pickle')
saveToPickle(trajList, trajectorySavePath)
# visualize
# physicsViewer.render()
visualize = True
if visualize:
entitiesColorList = [wolfColor] * numWolves + [sheepColor] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents, getPosFromAgentState)
render(trajList)
def generateSingleCondition(condition):
debug = 0
if debug:
damping=2.0
frictionloss=0.0
masterForce=1.0
numWolves = 1
numSheeps = 1
numMasters = 1
maxTimeStep = 25
saveTraj=False
saveImage=True
visualizeMujoco=False
visualizeTraj = True
makeVideo=True
else:
# print(sys.argv)
# condition = json.loads(sys.argv[1])
damping = float(condition['damping'])
frictionloss = float(condition['frictionloss'])
masterForce = float(condition['masterForce'])
numWolves = 1
numSheeps = 1
numMasters = 1
maxTimeStep = 25
saveTraj=False
saveImage=True
visualizeMujoco=False
visualizeTraj = True
makeVideo=False
print("maddpg: , saveTraj: {}, visualize: {},damping; {},frictionloss: {}".format( str(saveTraj), str(visualizeMujoco),damping,frictionloss))
numAgents = numWolves + numSheeps+numMasters
numEntities = numAgents + numMasters
wolvesID = [0]
sheepsID = [1]
masterID = [2]
wolfSize = 0.075
sheepSize = 0.05
masterSize = 0.075
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [masterSize] * numMasters
wolfMaxSpeed = 1.0
blockMaxSpeed = None
entitiesMovableList = [True] * numAgents + [False] * numMasters
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision,punishForOutOfBound)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
rewardMaster= lambda state, action, nextState: [-reward for reward in rewardWolf(state, action, nextState)]
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))+list(rewardMaster(state, action, nextState))
dirName = os.path.dirname(__file__)
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leased.xml')
makePropertyList=MakePropertyList(transferNumberListToStr)
geomIds=[1,2,3]
keyNameList=[0,1]
valueList=[[damping,damping]]*len(geomIds)
dampngParameter=makePropertyList(geomIds,keyNameList,valueList)
changeJointDampingProperty=lambda envDict,geomPropertyDict:changeJointProperty(envDict,geomPropertyDict,'@damping')
geomIds=[1,2,3]
keyNameList=[0,1]
valueList=[[frictionloss,frictionloss]]*len(geomIds)
frictionlossParameter=makePropertyList(geomIds,keyNameList,valueList)
changeJointFrictionlossProperty=lambda envDict,geomPropertyDict:changeJointProperty(envDict,geomPropertyDict,'@frictionloss')
physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leasedNew.xml')
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','leased.xml')
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlPropertyDictList=[dampngParameter,frictionlossParameter]
changeEnvXmlPropertFuntionyList=[changeJointDampingProperty,changeJointFrictionlossProperty]
for propertyDict,changeXmlProperty in zip(envXmlPropertyDictList,changeEnvXmlPropertFuntionyList):
envXmlDict=changeXmlProperty(envXmlDict,propertyDict)
envXml=xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# print(physicsSimulation.model.body_pos)
# print(dir(physicsSimulation.model))
# print(dir(physicsSimulation.data),physicsSimulation.dataphysicsSimulation.data)
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
qPosInit = (0, ) * 24
qVelInit = (0, ) * 24
qPosInitNoise = 0.4
qVelInitNoise = 0
numAgent = 2
tiedAgentId = [0, 2]
ropePartIndex = list(range(3, 12))
maxRopePartLength = 0.06
reset = ResetUniformWithoutXPosForLeashed(physicsSimulation, qPosInit, qVelInit, numAgent, tiedAgentId,ropePartIndex, maxRopePartLength, qPosInitNoise, qVelInitNoise)
numSimulationFrames=10
isTerminal= lambda state: False
reshapeActionList = [ReshapeAction(5),ReshapeAction(5),ReshapeAction(masterForce)]
transit=TransitionFunctionWithoutXPos(physicsSimulation, numSimulationFrames, visualizeMujoco,isTerminal, reshapeActionList)
# damping=2.5
# numSimulationFrames =int(0.1/dt)
# agentsMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps
# reshapeAction = ReshapeAction()
# isTerminal = lambda state: False
# transit = TransitionFunctionWithoutXPos(physicsSimulation,numAgents , numSimulationFrames,damping*dt*numSimulationFrames,agentsMaxSpeedList,visualizeMujoco,isTerminal,reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit, isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, masterID, getPosFromAgentState, getVelFromAgentState)
observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
initObsForParams = observe(reset())
obsShape = [initObsForParams[obsID].shape[0] for obsID in range(len(initObsForParams))]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)]
# individStr = 'individ' if individualRewardWolf else 'shared'
# fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
# numWolves, numSheeps, numMasters, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
# wolvesModelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg', fileName + str(i) + '60000eps') for i in wolvesID]
# [restoreVariables(model, path) for model, path in zip(wolvesModel, wolvesModelPaths)]
#
# actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
# policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
# modelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg_ExpEpsLengthAndSheepSpeed', fileName + str(i)) for i in
# range(numAgents)]
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG')
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','timeconst='+str(timeconst)+'dampratio='+str(dampratio))
modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPGLeasedFixedEnv2','damping={}_frictionloss={}_masterForce={}'.format(damping,frictionloss,masterForce))
fileName = "maddpg{}episodes{}step_agent".format(maxEpisode, maxTimeStep)
modelPaths = [os.path.join(modelFolder, fileName + str(i) + '60000eps') for i in range(numAgents)]
[restoreVariables(model, path) for model, path in zip(modelsList, modelPaths)]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
trajList = []
numTrajToSample = 5
for i in range(numTrajToSample):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
# saveTraj
if saveTraj:
trajFileName = "maddpg{}wolves{}sheep{}blocks{}eps{}stepSheepSpeed{}{}Traj".format(numWolves, numSheeps, numMasters, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
trajSavePath = os.path.join(dirName, '..', 'trajectory', trajFileName)
saveToPickle(trajList, trajSavePath)
# visualize
if visualizeTraj:
demoFolder = os.path.join(dirName, '..', 'demos', 'mujocoMADDPGLeasedFixedEnv2','damping={}_frictionloss={}_masterForce={}'.format(damping,frictionloss,masterForce))
if not os.path.exists(demoFolder):
os.makedirs(demoFolder)
entitiesColorList = [wolfColor] * numWolves + [sheepColor] * numSheeps + [masterColor] * numMasters
render = Render(entitiesSizeList, entitiesColorList, numAgents,demoFolder,saveImage, getPosFromAgentState)
# print(trajList[0][0],'!!!3',trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
def simuliateOneParameter(parameterDict):
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
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])
wolvesID = [0,1]
sheepsID = [2]
blocksID = [3]
numWolves = len(wolvesID)
numSheeps = len(sheepsID)
numBlocks = len(blocksID)
sheepMaxSpeed = 1.3
wolfMaxSpeed =1.0
blockMaxSpeed = None
agentsMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
entitiesSizeList = [wolfSize]* numWolves + [sheepSize] * numSheeps + [blockSize]* numBlocks
entityMaxSpeedList = [wolfMaxSpeed]* numWolves + [sheepMaxSpeed] * numSheeps + [blockMaxSpeed]* numBlocks
entitiesMovableList = [True]* numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision, punishForOutOfBound)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
makePropertyList=MakePropertyList(transferNumberListToStr)
#changeCollisionReleventParameter
dmin=parameterDict['dmin']
dmax=parameterDict['dmax']
width=parameterDict['width']
midpoint=parameterDict['midpoint']
power=parameterDict['power']
timeconst=parameterDict['timeconst']
dampratio=parameterDict['dampratio']
geomIds=[1,2,3,4]
keyNameList=['@solimp','@solref']
valueList=[[[dmin,dmax,width,midpoint,power],[timeconst,dampratio]]]*len(geomIds)
collisionParameter=makePropertyList(geomIds,keyNameList,valueList)
#changeSize
# geomIds=[1,2]
# keyNameList=['@size']
# valueList=[[[0.075,0.075]],[[0.1,0.1]]]
# sizeParameter=makePropertyList(geomIds,keyNameList,valueList)
#load env xml and change some geoms' property
physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','rope','1leased.xml')
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
# envXmlPropertyDictList=[collisionParameter]
# changeEnvXmlPropertFuntionyList=[changeGeomProperty]
# for propertyDict,changeXmlProperty in zip(envXmlPropertyDictList,changeEnvXmlPropertFuntionyList):
# envXmlDict=changeXmlProperty(envXmlDict,propertyDict)
envXml=xmltodict.unparse(envXmlDict)
# print(envXmlDict['mujoco']['worldbody']['body'][0])
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# MDP function
qPosInit = [0, 0]*numAgents
qVelInit = [0, 0]*numAgents
qVelInitNoise = 0
qPosInitNoise = 1
# reset=ResetUniformWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents, numBlocks,qPosInitNoise, qVelInitNoise)
fixReset=ResetFixWithoutXPos(physicsSimulation, qPosInit, qVelInit, numAgents,numBlocks)
blocksState=[[0,-0.8,0,0]]
reset=lambda :fixReset([-0.5,0.8,-0.5,0,0.5,0.8],[0,0,0,0,0,0],blocksState)
isTerminal = lambda state: False
numSimulationFrames = 10
visualize=True
# physicsViewer=None
dt=0.01
damping=2.5
reshapeAction=lambda action:action
physicsViewer = mujoco.MjViewer(physicsSimulation)
qPosInit = (0, ) * 24
qVelInit = (0, ) * 24
qPosInitNoise = 6
qVelInitNoise = 4
numAgent = 3
tiedAgentId = [1, 2]
ropePartIndex = list(range(3, 12))
maxRopePartLength = 0.6
reset = ResetUniformForLeashed(physicsSimulation, qPosInit, qVelInit, numAgent, tiedAgentId, \
ropePartIndex, maxRopePartLength, qPosInitNoise, qVelInitNoise)
reset()
for i in range(6000):
physicsViewer.render()
def main():
debug = 0
if debug:
numWolves = 3
numSheeps = 1
numBlocks = 2
saveAllmodels = False
maxTimeStep = 25
sheepSpeedMultiplier = 1
sampleMethod = '5'
learningRateSheepCritic = 0.005
learningRateSheepActor = 0.005
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = 3
numSheeps = 1
numBlocks = 2
saveAllmodels = False
maxTimeStep = 25
sheepSpeedMultiplier = 1
sampleMethod = condition['sampleMethod']
learningRateSheepCritic = condition['sheepLr']
learningRateSheepActor = condition['sheepLr']
print(
"maddpg: {} wolves, {} sheep, {} blocks, {} episodes with {} steps each eps, sheepSpeed: {}x, sampleMethod: {}"
.format(numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, str(sampleMethod)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
rewardWolfIndivid = RewardWolfIndividual(wolvesID, sheepsID,
entitiesSizeList, isCollision)
rewardWolfShared = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFuncIndividWolf = lambda state, action, nextState: \
list(rewardWolfIndivid(state, action, nextState)) + list(rewardSheep(state, action, nextState))
rewardFuncSharedWolf = lambda state, action, nextState: \
list(rewardWolfShared(state, action, nextState)) + list(rewardSheep(state, action, nextState))
reset = ResetMultiAgentChasing(numAgents, numBlocks)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
reshapeAction = ReshapeAction()
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, reshapeAction,
applyActionForce, applyEnvironForce,
integrateState)
isTerminal = lambda state: [False] * numAgents
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
#------------ models ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsListShared = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
sheepModel = [modelsListShared[sheepID] for sheepID in sheepsID]
modelsListIndivid = [
buildMADDPGModels(layerWidth, agentID) for agentID in wolvesID
] + sheepModel
trainCriticBySASRWolf = TrainCriticBySASR(
actByPolicyTargetNoisyForNextState, learningRateWolfCritic, gamma)
trainCriticWolf = TrainCritic(trainCriticBySASRWolf)
trainCriticBySASRSheep = TrainCriticBySASR(
actByPolicyTargetNoisyForNextState, learningRateSheepCritic, gamma)
trainCriticSheep = TrainCritic(trainCriticBySASRSheep)
trainActorFromSAWolf = TrainActorFromSA(learningRateWolfActor)
trainActorWolf = TrainActor(trainActorFromSAWolf)
trainActorFromSASheep = TrainActorFromSA(learningRateSheepActor)
trainActorSheep = TrainActor(trainActorFromSASheep)
trainActorList = [trainActorWolf] * numWolves + [trainActorSheep
] * numSheeps
trainCriticList = [trainCriticWolf] * numWolves + [trainCriticSheep
] * numSheeps
paramUpdateInterval = 1 #
updateParameters = UpdateParameters(paramUpdateInterval, tau)
sampleBatchFromMemory = SampleFromMemory(minibatchSize)
learnInterval = 100
learningStartBufferSize = minibatchSize * maxTimeStep
startLearn = StartLearn(learningStartBufferSize, learnInterval)
trainMADDPGModelsIndivid = TrainMADDPGModelsWithIterSheep(
updateParameters, trainActorList, trainCriticList,
sampleBatchFromMemory, startLearn, modelsListIndivid)
trainMADDPGModelsShared = TrainMADDPGModelsWithIterSheep(
updateParameters, trainActorList, trainCriticList,
sampleBatchFromMemory, startLearn, modelsListShared)
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
actOneStepIndivid = lambda allAgentsStates, runTime: [
actOneStepOneModel(model, allAgentsStates)
for model in modelsListIndivid
]
actOneStepShared = lambda allAgentsStates, runTime: [
actOneStepOneModel(model, allAgentsStates)
for model in modelsListShared
]
sampleOneStepIndivid = SampleOneStep(transit, rewardFuncIndividWolf)
sampleOneStepShared = SampleOneStep(transit, rewardFuncSharedWolf)
runDDPGTimeStepIndivid = RunTimeStep(actOneStepIndivid,
sampleOneStepIndivid,
trainMADDPGModelsIndivid,
observe=observe)
runDDPGTimeStepShared = RunTimeStep(actOneStepShared,
sampleOneStepShared,
trainMADDPGModelsShared,
observe=observe)
runEpisodeIndivid = RunEpisode(reset, runDDPGTimeStepIndivid, maxTimeStep,
isTerminal)
runEpisodeShared = RunEpisode(reset, runDDPGTimeStepShared, maxTimeStep,
isTerminal)
getAgentModelIndivid = lambda agentId: lambda: trainMADDPGModelsIndivid.getTrainedModels(
)[agentId]
getModelListIndivid = [getAgentModelIndivid(i) for i in range(numAgents)]
modelSaveRate = 1000
individStr = 'individ'
fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}Lr{}SampleMethod{}{}_agent".format(
numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, learningRateSheepActor, sampleMethod, individStr)
modelPath = os.path.join(dirName, '..', 'trainedModels',
'IterTrainSheep_evalSheeplrAndSampleMethod',
fileName)
saveModelsIndivid = [
SaveModel(modelSaveRate, saveVariables, getTrainedModel,
modelPath + str(i), saveAllmodels)
for i, getTrainedModel in enumerate(getModelListIndivid)
]
getAgentModelShared = lambda agentId: lambda: trainMADDPGModelsShared.getTrainedModels(
)[agentId]
getModelListShared = [getAgentModelShared(i) for i in range(numAgents)]
individStr = 'shared'
fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}Lr{}SampleMethod{}{}_agent".format(
numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, learningRateSheepActor, sampleMethod, individStr)
modelPath = os.path.join(dirName, '..', 'trainedModels',
'IterTrainSheep_evalSheeplrAndSampleMethod',
fileName)
saveModelsShared = [
SaveModel(modelSaveRate, saveVariables, getTrainedModel,
modelPath + str(i), saveAllmodels)
for i, getTrainedModel in enumerate(getModelListShared)
]
maddpgIterSheep = RunAlgorithmWithIterSheep(runEpisodeIndivid,
runEpisodeShared, maxEpisode,
saveModelsIndivid,
saveModelsShared, sampleMethod,
numAgents)
replayBufferIndivid = getBuffer(bufferSize)
replayBufferShared = getBuffer(bufferSize)
meanRewardList, trajectory = maddpgIterSheep(replayBufferShared,
replayBufferIndivid)
def main():
debug = 1
if debug:
numWolves = 2
numSheeps = 1
numBlocks = 1
saveAllmodels = True
maxTimeStep = 25
sheepSpeedMultiplier = 1
individualRewardWolf = int(False)
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = int(condition['numWolves'])
numSheeps = int(condition['numSheeps'])
numBlocks = int(condition['numBlocks'])
maxTimeStep = int(condition['maxTimeStep'])
sheepSpeedMultiplier = float(condition['sheepSpeedMultiplier'])
individualRewardWolf = int(condition['individualRewardWolf'])
saveAllmodels = False
print("maddpg: {} wolves, {} sheep, {} blocks, {} episodes with {} steps each eps, sheepSpeed: {}x, wolfIndividualReward: {}, save all models: {}".
format(numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individualRewardWolf, str(saveAllmodels)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [blockSize] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList, getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList, isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
reset = ResetMultiAgentChasing(numAgents, numBlocks)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID, blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [observeOneAgent(agentID)(state) for agentID in range(numAgents)]
reshapeAction = ReshapeAction()
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, reshapeAction, applyActionForce, applyEnvironForce, integrateState)
isTerminal = lambda state: [False]* numAgents
initObsForParams = observe(reset())
obsShape = [initObsForParams[obsID].shape[0] for obsID in range(len(initObsForParams))]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
#------------ models ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)]
trainCriticBySASR = TrainCriticBySASR(actByPolicyTargetNoisyForNextState, learningRateCritic, gamma)
trainCritic = TrainCritic(trainCriticBySASR)
trainActorFromSA = TrainActorFromSA(learningRateActor)
trainActor = TrainActor(trainActorFromSA)
paramUpdateInterval = 1 #
updateParameters = UpdateParameters(paramUpdateInterval, tau)
sampleBatchFromMemory = SampleFromMemory(minibatchSize)
learnInterval = 100
learningStartBufferSize = minibatchSize * maxTimeStep
startLearn = StartLearn(learningStartBufferSize, learnInterval)
trainMADDPGModels = TrainMADDPGModelsWithBuffer(updateParameters, trainActor, trainCritic, sampleBatchFromMemory, startLearn, modelsList)
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
actOneStep = lambda allAgentsStates, runTime: [actOneStepOneModel(model, allAgentsStates) for model in modelsList]
sampleOneStep = SampleOneStep(transit, rewardFunc)
runDDPGTimeStep = RunTimeStep(actOneStep, sampleOneStep, trainMADDPGModels, observe = observe)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep, isTerminal)
getAgentModel = lambda agentId: lambda: trainMADDPGModels.getTrainedModels()[agentId]
getModelList = [getAgentModel(i) for i in range(numAgents)]
modelSaveRate = 1000
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
modelPath = os.path.join(dirName, '..', 'trainedModels', 'maddpg', fileName)
saveModels = [SaveModel(modelSaveRate, saveVariables, getTrainedModel, modelPath+ str(i), saveAllmodels) for i, getTrainedModel in enumerate(getModelList)]
maddpg = RunAlgorithm(runEpisode, maxEpisode, saveModels, numAgents)
replayBuffer = getBuffer(bufferSize)
meanRewardList, trajectory = maddpg(replayBuffer)
def main():
debug = 1
if debug:
numWolves = 3
numSheeps = 1
numBlocks = 2
timeconst = 0.5
dampratio = 0.2
saveTraj = False
visualizeTraj = True
maxTimeStep = 25
sheepSpeedMultiplier = 1.0
individualRewardWolf = 0
hasWalls = 1.5
dt = 0.05
visualizeMujoco = True
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = int(condition['numWolves'])
numSheeps = int(condition['numSheeps'])
numBlocks = int(condition['numBlocks'])
saveTraj = True
visualizeTraj = False
maxTimeStep = 50
sheepSpeedMultiplier = 1.25
individualRewardWolf = 1
print(
"maddpg: {} wolves, {} sheep, {} blocks, saveTraj: {}, visualize: {}".
format(numWolves, numSheeps, numBlocks, str(saveTraj),
str(visualizeTraj)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFunc = lambda state, action, nextState: list(
rewardWolf(state, action, nextState)) + list(
rewardSheep(state, action, nextState))
dirName = os.path.dirname(__file__)
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','numBlocks=1_numSheeps=1_numWolves=1.xml')
if hasWalls:
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','hasWalls=1_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'.format(numBlocks,numSheeps,numWolves,timeconst,dampratio))
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(hasWalls, numBlocks, numSheeps, numWolves, timeconst,
dampratio))
# physicsDynamicsPath=os.path.join(dirName,'..','..','environment','mujocoEnv','crossTest3w1s2b.xml')
else:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(numBlocks, numSheeps, numWolves, timeconst, dampratio))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
print(physicsSimulation.model.body_pos)
# print(dir(physicsSimulation.model))
# print(dir(physicsSimulation.data),physicsSimulation.dataphysicsSimulation.data)
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
# print(physicsSimulation.data.qpos,dir(physicsSimulation.data.qpos))
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0 * hasWalls
qPosInitNoise = 0.8 * hasWalls
getBlockRandomPos = lambda: np.random.uniform(-0.7 * hasWalls, +0.7 *
hasWalls, 2)
getBlockSpeed = lambda: np.zeros(2)
numQPos = len(physicsSimulation.data.qpos)
numQVel = len(physicsSimulation.data.qvel)
sampleAgentsQPos = lambda: np.asarray(qPosInit) + np.random.uniform(
low=-qPosInitNoise, high=qPosInitNoise, size=numQPos)
sampleAgentsQVel = lambda: np.asarray(qVelInit) + np.random.uniform(
low=-qVelInitNoise, high=qVelInitNoise, size=numQVel)
minDistance = 0.2 + 2 * blockSize
isOverlap = IsOverlap(minDistance)
sampleBlockState = SampleBlockState(numBlocks, getBlockRandomPos,
getBlockSpeed, isOverlap)
reset = ResetUniformWithoutXPos(physicsSimulation, numAgents, numBlocks,
sampleAgentsQPos, sampleAgentsQVel,
sampleBlockState)
damping = 2.5
numSimulationFrames = int(0.1 / dt)
agentsMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed
] * numSheeps
reshapeAction = ReshapeAction()
isTerminal = lambda state: False
transit = TransitionFunctionWithoutXPos(physicsSimulation, numAgents,
numSimulationFrames,
damping * dt * numSimulationFrames,
agentsMaxSpeedList,
visualizeMujoco, isTerminal,
reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
# individStr = 'individ' if individualRewardWolf else 'shared'
# fileName = "maddpg{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
# numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep, sheepSpeedMultiplier, individStr)
# wolvesModelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg', fileName + str(i) + '60000eps') for i in wolvesID]
# [restoreVariables(model, path) for model, path in zip(wolvesModel, wolvesModelPaths)]
#
# actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
# policy = lambda allAgentsStates: [actOneStepOneModel(model, observe(allAgentsStates)) for model in modelsList]
# modelPaths = [os.path.join(dirName, '..', 'trainedModels', '3wolvesMaddpg_ExpEpsLengthAndSheepSpeed', fileName + str(i)) for i in
# range(numAgents)]
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG')
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','timeconst='+str(timeconst)+'dampratio='+str(dampratio))
if hasWalls:
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','hasWalls=1'+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio))
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','dt={}'.format(dt),'hasWalls='+str(hasWalls)+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio)+'individualRewardWolf='+str(individualRewardWolf)+'sheepSpeedMultiplier='+str(sheepSpeedMultiplier))
# modelFolder = os.path.join(dirName, '..', 'trainedModels', 'mujocoMADDPG','dt={}'.format(dt),'hasWalls='+str(hasWalls)+'numBlocks='+str(numBlocks)+'numSheeps='+str(numSheeps)+'numWolves='+str(numWolves)+'timeconst='+str(timeconst)+'dampratio='+str(dampratio)+'individualRewardWolf='+str(individualRewardWolf)+'sheepSpeedMultiplier='+str(sheepSpeedMultiplier))
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPGeavluateWall',
'dt={}'.format(dt),
'hasWalls=' + str(hasWalls) + 'numBlocks=' + str(numBlocks) +
'numSheeps=' + str(numSheeps) + 'numWolves=' + str(numWolves) +
'timeconst=' + str(timeconst) + 'dampratio=' + str(dampratio) +
'individualRewardWolf=' + str(individualRewardWolf) +
'sheepSpeedMultiplier=' + str(sheepSpeedMultiplier))
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpghasWalls={}{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
hasWalls, numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
else:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPG',
'numBlocks=' + str(numBlocks) + 'numSheeps=' + str(numSheeps) +
'numWolves=' + str(numWolves) + 'timeconst=' + str(timeconst) +
'dampratio=' + str(dampratio))
fileName = "maddpg{}wolves{}sheep{}blocks60000episodes25stepSheepSpeed1.0shared_agent".format(
numWolves, numSheeps, numBlocks)
modelPaths = [
os.path.join(modelFolder, fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
trajList = []
numTrajToSample = 50
for i in range(numTrajToSample):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
# saveTraj
if saveTraj:
trajFileName = "maddpg{}wolves{}sheep{}blocks{}eps{}stepSheepSpeed{}{}Traj".format(
numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
trajSavePath = os.path.join(dirName, '..', 'trajectory', trajFileName)
saveToPickle(trajList, trajSavePath)
# visualize
if visualizeTraj:
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
print(trajList[0][0], '!!!3', trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
def main():
debug = 0
if debug:
damping = 0.0
frictionloss = 0.4
masterForce = 1.0
numWolves = 1
numSheeps = 1
numMasters = 1
saveAllmodels = True
maxTimeStep = 25
visualize = False
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = 1
numSheeps = 1
numMasters = 1
damping = float(condition['damping'])
frictionloss = float(condition['frictionloss'])
masterForce = float(condition['masterForce'])
maxTimeStep = 25
visualize = False
saveAllmodels = True
print(
"maddpg: {} wolves, {} sheep, {} blocks, {} episodes with {} steps each eps, save all models: {}"
.format(numWolves, numSheeps, numMasters, maxEpisode, maxTimeStep,
str(saveAllmodels)))
print(damping, frictionloss, masterForce)
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPGLeasedFixedEnv2',
'damping={}_frictionloss={}_masterForce={}'.format(
damping, frictionloss, masterForce))
if not os.path.exists(modelFolder):
os.makedirs(modelFolder)
numAgents = numWolves + numSheeps + numMasters
numEntities = numAgents + numMasters
wolvesID = [0]
sheepsID = [1]
masterID = [2]
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.075
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numMasters
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList, isCollision)
rewardMaster = lambda state, action, nextState: [
-reward for reward in rewardWolf(state, action, nextState)
]
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))+list(rewardMaster(state, action, nextState))
makePropertyList = MakePropertyList(transferNumberListToStr)
geomIds = [1, 2, 3]
keyNameList = [0, 1]
valueList = [[damping, damping]] * len(geomIds)
dampngParameter = makePropertyList(geomIds, keyNameList, valueList)
changeJointDampingProperty = lambda envDict, geomPropertyDict: changeJointProperty(
envDict, geomPropertyDict, '@damping')
geomIds = [1, 2, 3]
keyNameList = [0, 1]
valueList = [[frictionloss, frictionloss]] * len(geomIds)
frictionlossParameter = makePropertyList(geomIds, keyNameList, valueList)
changeJointFrictionlossProperty = lambda envDict, geomPropertyDict: changeJointProperty(
envDict, geomPropertyDict, '@frictionloss')
physicsDynamicsPath = os.path.join(dirName, '..', '..', 'environment',
'mujocoEnv', 'rope', 'leasedNew.xml')
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXmlPropertyDictList = [dampngParameter, frictionlossParameter]
changeEnvXmlPropertFuntionyList = [
changeJointDampingProperty, changeJointFrictionlossProperty
]
for propertyDict, changeXmlProperty in zip(
envXmlPropertyDictList, changeEnvXmlPropertFuntionyList):
envXmlDict = changeXmlProperty(envXmlDict, propertyDict)
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
qPosInit = (0, ) * 24
qVelInit = (0, ) * 24
qPosInitNoise = 0.6
qVelInitNoise = 0
numAgent = 3
tiedAgentId = [0, 2]
ropePartIndex = list(range(3, 12))
maxRopePartLength = 0.06
reset = ResetUniformWithoutXPosForLeashed(physicsSimulation, qPosInit,
qVelInit, numAgent, tiedAgentId,
ropePartIndex, maxRopePartLength,
qPosInitNoise, qVelInitNoise)
numSimulationFrames = 10
isTerminal = lambda state: False
reshapeActionList = [
ReshapeAction(5),
ReshapeAction(5),
ReshapeAction(masterForce)
]
transit = TransitionFunctionWithoutXPos(physicsSimulation,
numSimulationFrames, visualize,
isTerminal, reshapeActionList)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
masterID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
#------------ models ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
trainCriticBySASR = TrainCriticBySASR(actByPolicyTargetNoisyForNextState,
learningRateCritic, gamma)
trainCritic = TrainCritic(trainCriticBySASR)
trainActorFromSA = TrainActorFromSA(learningRateActor)
trainActor = TrainActor(trainActorFromSA)
paramUpdateInterval = 1 #
updateParameters = UpdateParameters(paramUpdateInterval, tau)
sampleBatchFromMemory = SampleFromMemory(minibatchSize)
learnInterval = 100
learningStartBufferSize = minibatchSize * maxTimeStep
startLearn = StartLearn(learningStartBufferSize, learnInterval)
trainMADDPGModels = TrainMADDPGModelsWithBuffer(updateParameters,
trainActor, trainCritic,
sampleBatchFromMemory,
startLearn, modelsList)
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
actOneStep = lambda allAgentsStates, runTime: [
actOneStepOneModel(model, allAgentsStates) for model in modelsList
]
sampleOneStep = SampleOneStep(transit, rewardFunc)
runDDPGTimeStep = RunTimeStep(actOneStep,
sampleOneStep,
trainMADDPGModels,
observe=observe)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep, isTerminal)
getAgentModel = lambda agentId: lambda: trainMADDPGModels.getTrainedModels(
)[agentId]
getModelList = [getAgentModel(i) for i in range(numAgents)]
modelSaveRate = 1000
fileName = "maddpg{}episodes{}step_agent".format(maxEpisode, maxTimeStep)
modelPath = os.path.join(modelFolder, fileName)
saveModels = [
SaveModel(modelSaveRate, saveVariables, getTrainedModel,
modelPath + str(i), saveAllmodels)
for i, getTrainedModel in enumerate(getModelList)
]
maddpg = RunAlgorithm(runEpisode, maxEpisode, saveModels, numAgents)
replayBuffer = getBuffer(bufferSize)
meanRewardList, trajectory = maddpg(replayBuffer)
def simuliateOneParameter(parameterOneCondiiton, evalNum, randomSeed):
saveTraj = True
visualizeTraj = False
visualizeMujoco = False
numWolves = parameterOneCondiiton['numWolves']
numSheeps = parameterOneCondiiton['numSheeps']
numBlocks = parameterOneCondiiton['numBlocks']
timeconst = parameterOneCondiiton['timeconst']
dampratio = parameterOneCondiiton['dampratio']
maxTimeStep = parameterOneCondiiton['maxTimeStep']
sheepSpeedMultiplier = parameterOneCondiiton['sheepSpeedMultiplier']
individualRewardWolf = parameterOneCondiiton['individualRewardWolf']
hasWalls = parameterOneCondiiton['hasWalls']
dt = parameterOneCondiiton['dt']
print(
"maddpg: {} wolves, {} sheep, {} blocks, saveTraj: {}, visualize: {}".
format(numWolves, numSheeps, numBlocks, str(saveTraj),
str(visualizeTraj)))
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
wolfMaxSpeed = 1.0
blockMaxSpeed = None
sheepMaxSpeedOriginal = 1.3
sheepMaxSpeed = sheepMaxSpeedOriginal * sheepSpeedMultiplier
entityMaxSpeedList = [wolfMaxSpeed] * numWolves + [
sheepMaxSpeed
] * numSheeps + [blockMaxSpeed] * numBlocks
entitiesMovableList = [True] * numAgents + [False] * numBlocks
massList = [1.0] * numEntities
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFunc = lambda state, action, nextState: list(
rewardWolf(state, action, nextState)) + list(
rewardSheep(state, action, nextState))
dirName = os.path.dirname(__file__)
if hasWalls:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(hasWalls, numBlocks, numSheeps, numWolves, timeconst,
dampratio))
else:
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv',
'numBlocks={}_numSheeps={}_numWolves={}timeconst={}dampratio={}.xml'
.format(numBlocks, numSheeps, numWolves, timeconst, dampratio))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
# print(physicsSimulation.model.body_pos)
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0 * hasWalls
qPosInitNoise = 0.8 * hasWalls
getBlockRandomPos = lambda: np.random.uniform(-0.7 * hasWalls, +0.7 *
hasWalls, 2)
getBlockSpeed = lambda: np.zeros(2)
numQPos = len(physicsSimulation.data.qpos)
numQVel = len(physicsSimulation.data.qvel)
sampleAgentsQPos = lambda: np.asarray(qPosInit) + np.random.uniform(
low=-qPosInitNoise, high=qPosInitNoise, size=numQPos)
sampleAgentsQVel = lambda: np.asarray(qVelInit) + np.random.uniform(
low=-qVelInitNoise, high=qVelInitNoise, size=numQVel)
minDistance = 0.2 + 2 * blockSize
isOverlap = IsOverlap(minDistance)
sampleBlockState = SampleBlockState(numBlocks, getBlockRandomPos,
getBlockSpeed, isOverlap)
reset = ResetUniformWithoutXPos(physicsSimulation, numAgents, numBlocks,
sampleAgentsQPos, sampleAgentsQVel,
sampleBlockState)
damping = 2.5
numSimulationFrames = int(0.1 / dt)
agentsMaxSpeedList = [wolfMaxSpeed] * numWolves + [sheepMaxSpeed
] * numSheeps
reshapeAction = ReshapeAction()
isTerminal = lambda state: False
transit = TransitionFunctionWithoutXPos(physicsSimulation, numAgents,
numSimulationFrames,
damping * dt * numSimulationFrames,
agentsMaxSpeedList,
visualizeMujoco, isTerminal,
reshapeAction)
maxRunningStepsToSample = 100
sampleTrajectory = SampleTrajectory(maxRunningStepsToSample, transit,
isTerminal, rewardFunc, reset)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
# ------------ model ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
if hasWalls:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPGeavluateWall',
'dt={}'.format(dt),
'hasWalls=' + str(hasWalls) + 'numBlocks=' + str(numBlocks) +
'numSheeps=' + str(numSheeps) + 'numWolves=' + str(numWolves) +
'timeconst=' + str(timeconst) + 'dampratio=' + str(dampratio) +
'individualRewardWolf=' + str(individualRewardWolf) +
'sheepSpeedMultiplier=' + str(sheepSpeedMultiplier))
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpghasWalls={}{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
hasWalls, numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
else:
modelFolder = os.path.join(
dirName, '..', 'trainedModels', 'mujocoMADDPG',
'numBlocks=' + str(numBlocks) + 'numSheeps=' + str(numSheeps) +
'numWolves=' + str(numWolves) + 'timeconst=' + str(timeconst) +
'dampratio=' + str(dampratio))
fileName = "maddpg{}wolves{}sheep{}blocks60000episodes25stepSheepSpeed1.0shared_agent".format(
numWolves, numSheeps, numBlocks)
modelPaths = [
os.path.join(modelFolder, fileName + str(i) + '60000eps')
for i in range(numAgents)
]
[
restoreVariables(model, path)
for model, path in zip(modelsList, modelPaths)
]
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
policy = lambda allAgentsStates: [
actOneStepOneModel(model, observe(allAgentsStates))
for model in modelsList
]
startTime = time.time()
trajList = []
for i in range(evalNum):
np.random.seed(i)
traj = sampleTrajectory(policy)
trajList.append(list(traj))
endTime = time.time()
print("Time taken {} seconds to generate {} trajectories".format(
(endTime - startTime), evalNum))
# saveTraj
if saveTraj:
trajectoryFolder = os.path.join(dirName, '..', 'trajectory',
'evluateWall')
if not os.path.exists(trajectoryFolder):
os.makedirs(trajectoryFolder)
trajectorySaveExtension = '.pickle'
fixedParameters = {'randomSeed': randomSeed, 'evalNum': evalNum}
generateTrajectorySavePath = GetSavePath(trajectoryFolder,
trajectorySaveExtension,
fixedParameters)
trajectorySavePath = generateTrajectorySavePath(parameterOneCondiiton)
saveToPickle(trajList, trajectorySavePath)
# visualize
if visualizeTraj:
entitiesColorList = [wolfColor] * numWolves + [
sheepColor
] * numSheeps + [blockColor] * numBlocks
render = Render(entitiesSizeList, entitiesColorList, numAgents,
getPosFromAgentState)
print(trajList[0][0], '!!!3', trajList[0][1])
trajToRender = np.concatenate(trajList)
render(trajToRender)
return endTime - startTime
def main():
debug = 1
if debug:
numWolves = 2
numSheeps = 4
numBlocks = 2
hasWalls = 1.0
dt = 0.02
maxTimeStep = 25
sheepSpeedMultiplier = 1.0
individualRewardWolf = int(False)
mujocoVisualize = False
saveAllmodels = True
else:
print(sys.argv)
condition = json.loads(sys.argv[1])
numWolves = int(condition['numWolves'])
numSheeps = int(condition['numSheeps'])
numBlocks = int(condition['numBlocks'])
hasWalls = float(condition['hasWalls'])
dt = float(condition['dt'])
maxTimeStep = int(condition['maxTimeStep'])
sheepSpeedMultiplier = float(condition['sheepSpeedMultiplier'])
individualRewardWolf = int(condition['individualRewardWolf'])
saveAllmodels = True
mujocoVisualize = False
print(
"maddpg: {} wolves, {} sheep, {} blocks, {} episodes with {} steps each eps, sheepSpeed: {}x, wolfIndividualReward: {}, save all models: {}"
.format(numWolves, numSheeps, numBlocks, maxEpisode,
maxTimeStep, sheepSpeedMultiplier, individualRewardWolf,
str(saveAllmodels)))
dataMainFolder = os.path.join(dirName, '..', 'trainedModels',
'mujocoMADDPG')
modelFolder = os.path.join(
dataMainFolder, 'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}_individualRewardWolf={}_sheepSpeedMultiplier={}.xml'
.format(hasWalls, numBlocks, numSheeps, numWolves,
individualRewardWolf, sheepSpeedMultiplier))
if not os.path.exists(modelFolder):
os.makedirs(modelFolder)
numAgents = numWolves + numSheeps
numEntities = numAgents + numBlocks
wolvesID = list(range(numWolves))
sheepsID = list(range(numWolves, numAgents))
blocksID = list(range(numAgents, numEntities))
wolfSize = 0.075
sheepSize = 0.05
blockSize = 0.2
entitiesSizeList = [wolfSize] * numWolves + [sheepSize] * numSheeps + [
blockSize
] * numBlocks
isCollision = IsCollision(getPosFromAgentState)
punishForOutOfBound = lambda state: 0 #PunishForOutOfBound()
rewardSheep = RewardSheep(wolvesID, sheepsID, entitiesSizeList,
getPosFromAgentState, isCollision,
punishForOutOfBound)
if individualRewardWolf:
rewardWolf = RewardWolfIndividual(wolvesID, sheepsID, entitiesSizeList,
isCollision)
else:
rewardWolf = RewardWolf(wolvesID, sheepsID, entitiesSizeList,
isCollision)
rewardFunc = lambda state, action, nextState: \
list(rewardWolf(state, action, nextState)) + list(rewardSheep(state, action, nextState))
#------------ mujocoEnv ------------------------
physicsDynamicsPath = os.path.join(
dirName, '..', '..', 'environment', 'mujocoEnv', 'dt={}'.format(dt),
'hasWalls={}_numBlocks={}_numSheeps={}_numWolves={}.xml'.format(
hasWalls, numBlocks, numSheeps, numWolves))
with open(physicsDynamicsPath) as f:
xml_string = f.read()
envXmlDict = xmltodict.parse(xml_string.strip())
envXml = xmltodict.unparse(envXmlDict)
physicsModel = mujoco.load_model_from_xml(envXml)
physicsSimulation = mujoco.MjSim(physicsModel)
qPosInit = [0, 0] * numAgents
qVelInit = [0, 0] * numAgents
qVelInitNoise = 0 * hasWalls
qPosInitNoise = 0.8 * hasWalls
getBlockRandomPos = lambda: np.random.uniform(-0.7 * hasWalls, +0.7 *
hasWalls, 2)
getBlockSpeed = lambda: np.zeros(2)
numQPos = len(physicsSimulation.data.qpos)
numQVel = len(physicsSimulation.data.qvel)
sampleAgentsQPos = lambda: np.asarray(qPosInit) + np.random.uniform(
low=-qPosInitNoise, high=qPosInitNoise, size=numQPos)
sampleAgentsQVel = lambda: np.asarray(qVelInit) + np.random.uniform(
low=-qVelInitNoise, high=qVelInitNoise, size=numQVel)
minDistance = 0.2 + 2 * blockSize #>2*wolfSize+2*blockSize
isOverlap = IsOverlap(minDistance)
sampleBlockState = SampleBlockState(numBlocks, getBlockRandomPos,
getBlockSpeed, isOverlap)
reset = ResetUniformWithoutXPos(physicsSimulation, numAgents, numBlocks,
sampleAgentsQPos, sampleAgentsQVel,
sampleBlockState)
transitTimePerStep = 0.1
numSimulationFrames = int(transitTimePerStep / dt)
isTerminal = lambda state: [False] * numAgents
reshapeAction = ReshapeAction()
transit = TransitionFunction(physicsSimulation, numAgents,
numSimulationFrames, mujocoVisualize,
isTerminal, reshapeAction)
observeOneAgent = lambda agentID: Observe(agentID, wolvesID, sheepsID,
blocksID, getPosFromAgentState,
getVelFromAgentState)
observe = lambda state: [
observeOneAgent(agentID)(state) for agentID in range(numAgents)
]
initObsForParams = observe(reset())
obsShape = [
initObsForParams[obsID].shape[0]
for obsID in range(len(initObsForParams))
]
worldDim = 2
actionDim = worldDim * 2 + 1
layerWidth = [128, 128]
#------------ models ------------------------
buildMADDPGModels = BuildMADDPGModels(actionDim, numAgents, obsShape)
modelsList = [
buildMADDPGModels(layerWidth, agentID) for agentID in range(numAgents)
]
trainCriticBySASR = TrainCriticBySASR(actByPolicyTargetNoisyForNextState,
learningRateCritic, gamma)
trainCritic = TrainCritic(trainCriticBySASR)
trainActorFromSA = TrainActorFromSA(learningRateActor)
trainActor = TrainActor(trainActorFromSA)
paramUpdateInterval = 1 #
updateParameters = UpdateParameters(paramUpdateInterval, tau)
sampleBatchFromMemory = SampleFromMemory(minibatchSize)
learnInterval = 100
learningStartBufferSize = minibatchSize * maxTimeStep
startLearn = StartLearn(learningStartBufferSize, learnInterval)
trainMADDPGModels = TrainMADDPGModelsWithBuffer(updateParameters,
trainActor, trainCritic,
sampleBatchFromMemory,
startLearn, modelsList)
actOneStepOneModel = ActOneStep(actByPolicyTrainNoisy)
actOneStep = lambda allAgentsStates, runTime: [
actOneStepOneModel(model, allAgentsStates) for model in modelsList
]
sampleOneStep = SampleOneStep(transit, rewardFunc)
runDDPGTimeStep = RunTimeStep(actOneStep,
sampleOneStep,
trainMADDPGModels,
observe=observe)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep, isTerminal)
getAgentModel = lambda agentId: lambda: trainMADDPGModels.getTrainedModels(
)[agentId]
getModelList = [getAgentModel(i) for i in range(numAgents)]
modelSaveRate = 1000
individStr = 'individ' if individualRewardWolf else 'shared'
fileName = "maddpghasWalls={}{}wolves{}sheep{}blocks{}episodes{}stepSheepSpeed{}{}_agent".format(
hasWalls, numWolves, numSheeps, numBlocks, maxEpisode, maxTimeStep,
sheepSpeedMultiplier, individStr)
modelPath = os.path.join(modelFolder, fileName)
saveModels = [
SaveModel(modelSaveRate, saveVariables, getTrainedModel,
modelPath + str(i), saveAllmodels)
for i, getTrainedModel in enumerate(getModelList)
]
maddpg = RunAlgorithm(runEpisode, maxEpisode, saveModels, numAgents)
replayBuffer = getBuffer(bufferSize)
meanRewardList, trajectory = maddpg(replayBuffer)