def testActorTrainImprovement(self):
stateBatch = [[2, 5, 10, 5, 2, 5, 10, 5], [1, 1, 1, 1, 1, 1, 1, 1]]
actionBatch = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.2, 0.2, 0.2, 0.2]]
rewardBatch = [[2], [0]]
targetQValue = [[3], [1]]
numStateSpace = len(stateBatch[0])
actionDim = 5
actionRange = 1
buildActorModel = BuildActorModel(numStateSpace, actionDim,
actionRange)
actorLayerWidths = [64, 64]
criticLayerWidths = [64, 64]
buildCriticModel = BuildCriticModel(numStateSpace, actionDim)
actorWriter, actorModel = buildActorModel(actorLayerWidths)
criticWriter, criticModel = buildCriticModel(criticLayerWidths)
trainCriticBySASRQ = TrainCriticBySASRQ(self.learningRateCritic,
self.gamma, criticWriter)
for i in range(100):
lossWithTrain, criticModel = trainCriticBySASRQ(
criticModel, stateBatch, actionBatch, rewardBatch,
targetQValue)
print(lossWithTrain)
actionUntrained = actByPolicyTrain(actorModel, stateBatch)
actionUntrainedQVal = evaluateCriticTrain(criticModel, stateBatch,
actionUntrained)
trainActorFromGradients = TrainActorFromGradients(
self.learningRateActor, actorWriter)
trainOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
actorModel = trainOneStep(actorModel, criticModel, stateBatch)
actionTrained = actByPolicyTrain(actorModel, stateBatch)
actionTrainedValue = evaluateCriticTrain(criticModel, stateBatch,
actionTrained)
[
self.assertTrue(trained > untrained) for trained, untrained in zip(
actionTrainedValue, actionUntrainedQVal)
]
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
dirName = os.path.dirname(__file__)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'Eps=300_High=0.4_actorModel=0_batch=128_env=MountainCarContinuous-v0_gam=0.9_lrActor=0.001_lrCritic=0.001_noiseVar=1_resetLow=-1_timeStep=2000_varDiscout=0.99995.ckpt'
)
restoreVariables(actorModel, actorModelPath)
policy = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
isTerminal = IsTerminalMountCarContin()
reset = ResetMountCarContin(seed=None, low=-1, high=0.4)
transit = TransitGymMountCarContinuous()
rewardFunc = RewardMountCarContin(isTerminal)
for i in range(20):
maxRunningSteps = 2000
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, rewardFunc, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeMountCarContin()
visualize(trajectory)
def setUp(self):
numAgents = 2
numStateSpace = numAgents * 2
actionLow = -np.pi
actionHigh = np.pi
actionRange = (actionHigh - actionLow) / 2.0
actionDim = 1
self.buildActorModel = BuildActorModel(numStateSpace, actionDim,
actionRange)
self.actorLayerWidths = [20, 20]
self.tau = 0.01
self.gamma = 0.95
self.learningRateActor = 0.0001
paramUpdateInterval = 1
self.updateParameters = UpdateParameters(paramUpdateInterval, self.tau)
self.buildCriticModel = BuildCriticModel(numStateSpace, actionDim)
self.criticLayerWidths = [10, 10]
self.learningRateCritic = 0.001
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'Eps=200_actorModel=0_batch=128_env=Pendulum-v0_gam=0.9_lrActor=0.001_lrCritic=0.001_noiseVar=3_timeStep=200_varDiscout=0.9995.ckpt'
)
restoreVariables(actorModel, actorModelPath)
actOneStep = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
policy = lambda state: actOneStep(observe(state))
isTerminal = isTerminalGymPendulum
reset = ResetGymPendulum(seed)
transit = TransitGymPendulum()
rewardFunc = RewardGymPendulum(angle_normalize)
for i in range(10):
maxRunningSteps = 200
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, rewardFunc, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeGymPendulum()
visualize(trajectory)
def main():
numAgents = 2
stateDim = numAgents * 2
actionLow = -1
actionHigh = 1
actionBound = (actionHigh - actionLow) / 2
actionDim = 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [64]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
dirName = os.path.dirname(__file__)
actorModelPath = os.path.join(
dirName, '..', 'trainedDDPGModels',
'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.001_learningRateCritic=0.001_maxEpisode=2000_maxTimeStep=20_minibatchSize=32_wolfSpeed=0.5.ckpt'
)
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=400_maxTimeStep=100_minibatchSize=32_wolfSpeed=1.ckpt')
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=1000_maxTimeStep=100_minibatchSize=32_wolfSpeed=0.5.ckpt')
# 'actorModel=0_dimension=2_gamma=0.95_learningRateActor=0.01_learningRateCritic=0.01_maxEpisode=5000_maxTimeStep=100_minibatchSize=32_wolfSpeed=0.5.ckpt')
restoreVariables(actorModel, actorModelPath)
sheepPolicy = ActDDPGOneStep(actionLow,
actionHigh,
actByPolicyTrain,
actorModel,
getNoise=None)
sheepId = 0
wolfId = 1
getSheepPos = GetAgentPosFromState(sheepId)
getWolfPos = GetAgentPosFromState(wolfId)
wolfSpeed = 0.5
wolfPolicy = HeatSeekingContinuousDeterministicPolicy(
getWolfPos, getSheepPos, wolfSpeed)
xBoundary = (0, 20)
yBoundary = (0, 20)
stayWithinBoundary = StayWithinBoundary(xBoundary, yBoundary)
transit = TransitForNoPhysics(getIntendedNextState, stayWithinBoundary)
# transit = TransitWithSingleWolf(physicalTransition, wolfPolicy)
maxTimeStep = 20
sheepAliveBonus = 1 / maxTimeStep
sheepTerminalPenalty = 20
killzoneRadius = 0
isTerminal = IsTerminal(getWolfPos, getSheepPos, killzoneRadius)
getBoundaryPunishment = GetBoundaryPunishment(xBoundary,
yBoundary,
sheepIndex=0,
punishmentVal=10)
rewardSheep = RewardFunctionCompete(sheepAliveBonus, sheepTerminalPenalty,
isTerminal)
rewardSheepWithBoundaryHeuristics = RewardSheepWithBoundaryHeuristics(
rewardSheep, getIntendedNextState, getBoundaryPunishment, getSheepPos)
getSheepAction = lambda actions: [
actions[sheepId * actionDim], actions[sheepId * actionDim + 1]
]
getReward = lambda state, action, nextState: rewardSheepWithBoundaryHeuristics(
state, getSheepAction(action), nextState)
policy = lambda state: list(sheepPolicy(state)) + list(wolfPolicy(state))
# reset = Reset(xBoundary, yBoundary, numAgents)
reset = lambda: np.array([10, 10, 15, 5])
for i in range(10):
maxRunningSteps = 50
sampleTrajectory = SampleTrajectory(maxRunningSteps, transit,
isTerminal, getReward, reset)
trajectory = sampleTrajectory(policy)
# plots& plot
showDemo = True
if showDemo:
observe = Observe(trajectory, numAgents)
fullScreen = False
screenWidth = 800
screenHeight = 800
screen = initializeScreen(fullScreen, screenWidth, screenHeight)
leaveEdgeSpace = 200
lineWidth = 3
xBoundary = [leaveEdgeSpace, screenWidth - leaveEdgeSpace * 2]
yBoundary = [leaveEdgeSpace, screenHeight - leaveEdgeSpace * 2]
screenColor = THECOLORS['black']
lineColor = THECOLORS['white']
drawBackground = DrawBackground(screen, screenColor, xBoundary,
yBoundary, lineColor, lineWidth)
circleSize = 10
positionIndex = [0, 1]
drawState = DrawState(screen, circleSize, positionIndex,
drawBackground)
numberOfAgents = 2
chasingColors = [THECOLORS['green'], THECOLORS['red']]
colorSpace = chasingColors[:numberOfAgents]
FPS = 60
chaseTrial = ChaseTrialWithTraj(FPS,
colorSpace,
drawState,
saveImage=True)
rawXRange = [0, 20]
rawYRange = [0, 20]
scaledXRange = [210, 590]
scaledYRange = [210, 590]
scaleTrajectory = ScaleTrajectory(positionIndex, rawXRange,
rawYRange, scaledXRange,
scaledYRange)
oldFPS = 5
adjustFPS = AdjustDfFPStoTraj(oldFPS, FPS)
getTrajectory = lambda rawTrajectory: scaleTrajectory(
adjustFPS(rawTrajectory))
positionList = [observe(index) for index in range(len(trajectory))]
positionListToDraw = getTrajectory(positionList)
currentDir = os.getcwd()
parentDir = os.path.abspath(os.path.join(currentDir, os.pardir))
imageFolderName = 'Demo'
saveImageDir = os.path.join(os.path.join(parentDir, 'chasingDemo'),
imageFolderName)
if not os.path.exists(saveImageDir):
os.makedirs(saveImageDir)
chaseTrial(numberOfAgents, positionListToDraw, saveImageDir)
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
buildCriticModel = BuildCriticModel(stateDim, actionDim)
criticLayerWidths = [30]
criticWriter, criticModel = buildCriticModel(criticLayerWidths)
trainCriticBySASRQ = TrainCriticBySASRQ(learningRateCritic, gamma,
criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(learningRateActor,
actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
paramUpdateInterval = 1
updateParameters = UpdateParameters(paramUpdateInterval, tau)
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor, trainCritic,
actorModel, criticModel)
noiseInitVariance = 3
varianceDiscount = .9995
noiseDecayStartStep = bufferSize
getNoise = GetExponentialDecayGaussNoise(noiseInitVariance,
varianceDiscount,
noiseDecayStartStep)
actOneStepWithNoise = ActDDPGOneStep(actionLow, actionHigh,
actByPolicyTrain, actorModel,
getNoise)
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
transit = TransitGymPendulum()
getReward = RewardGymPendulum(angle_normalize)
sampleOneStep = SampleOneStep(transit, getReward)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer, observe)
reset = ResetGymPendulum(seed)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep,
isTerminalGymPendulum)
dirName = os.path.dirname(__file__)
modelPath = os.path.join(dirName, '..', 'trainedDDPGModels', 'pendulum')
getTrainedModel = lambda: trainModels.actorModel
modelSaveRate = 50
saveModel = SaveModel(modelSaveRate, saveVariables, getTrainedModel,
modelPath)
ddpg = RunAlgorithm(runEpisode, maxEpisode, [saveModel])
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = ddpg(replayBuffer)
dirName = os.path.dirname(__file__)
trajectoryPath = os.path.join(dirName, '..', 'trajectory',
'pendulumTrajectory1.pickle')
saveToPickle(trajectory, trajectoryPath)
# plots& plot
showDemo = True
if showDemo:
visualize = VisualizeGymPendulum()
visualize(trajectory)
plotResult = True
if plotResult:
plt.plot(list(range(maxEpisode)), meanRewardList)
plt.show()
def __call__(self, df):
varianceDiscount = df.index.get_level_values('varianceDiscount')[0]
bufferSize = df.index.get_level_values('bufferSize')[0]
layerWidth = df.index.get_level_values('layerWidth')[0]
print('buffer: ', bufferSize, ', layers: ', layerWidth,
', varDiscount: ', varianceDiscount)
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorWriter, actorModel = buildActorModel(layerWidth)
buildCriticModel = BuildCriticModel(stateDim, actionDim)
criticWriter, criticModel = buildCriticModel(layerWidth)
trainCriticBySASRQ = TrainCriticBySASRQ(learningRateCritic, gamma,
criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(
learningRateActor, actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
paramUpdateInterval = 1
updateParameters = UpdateParameters(paramUpdateInterval, tau)
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor,
trainCritic, actorModel, criticModel)
noiseInitVariance = 1
noiseDecayStartStep = bufferSize
getNoise = GetExponentialDecayGaussNoise(noiseInitVariance,
varianceDiscount,
noiseDecayStartStep)
actOneStepWithNoise = ActDDPGOneStep(actionLow, actionHigh,
actByPolicyTrain, actorModel,
getNoise)
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
sheepId = 0
wolfId = 1
getSheepXPos = GetAgentPosFromState(sheepId)
getWolfXPos = GetAgentPosFromState(wolfId)
wolfSpeed = 2
wolfPolicy = HeatSeekingContinuousDeterministicPolicy(
getWolfXPos, getSheepXPos, wolfSpeed)
xBoundary = (0, 20)
yBoundary = (0, 20)
stayWithinBoundary = StayWithinBoundary(xBoundary, yBoundary)
physicalTransition = TransitForNoPhysics(getIntendedNextState,
stayWithinBoundary)
transit = TransitWithSingleWolf(physicalTransition, wolfPolicy)
sheepAliveBonus = 0 / maxTimeStep
sheepTerminalPenalty = -20
killzoneRadius = 1
isTerminal = IsTerminal(getWolfXPos, getSheepXPos, killzoneRadius)
getBoundaryPunishment = GetBoundaryPunishment(xBoundary,
yBoundary,
sheepIndex=0,
punishmentVal=10)
rewardSheep = RewardFunctionCompete(sheepAliveBonus,
sheepTerminalPenalty, isTerminal)
getReward = RewardSheepWithBoundaryHeuristics(rewardSheep,
getIntendedNextState,
getBoundaryPunishment,
getSheepXPos)
sampleOneStep = SampleOneStep(transit, getReward)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer)
reset = Reset(xBoundary, yBoundary, numAgents)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep,
isTerminal)
ddpg = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = ddpg(replayBuffer)
timeStep = list(range(len(meanRewardList)))
resultSe = pd.Series(
{time: reward
for time, reward in zip(timeStep, meanRewardList)})
return resultSe
def main():
numAgents = 2
stateDim = numAgents * 2
actionLow = -1
actionHigh = 1
actionBound = (actionHigh - actionLow) / 2
actionDim = 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [64]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
buildCriticModel = BuildCriticModel(stateDim, actionDim)
criticLayerWidths = [64]
criticWriter, criticModel = buildCriticModel(criticLayerWidths)
trainCriticBySASRQ = TrainCriticBySASRQ(learningRateCritic, gamma,
criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(learningRateActor,
actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
paramUpdateInterval = 1
updateParameters = UpdateParameters(paramUpdateInterval, tau)
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor, trainCritic,
actorModel, criticModel)
noiseInitVariance = 1
varianceDiscount = .9995
getNoise = GetExponentialDecayGaussNoise(noiseInitVariance,
varianceDiscount,
noiseDecayStartStep)
actOneStepWithNoise = ActDDPGOneStep(actionLow, actionHigh,
actByPolicyTrain, actorModel,
getNoise)
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
sheepId = 0
wolfId = 1
getSheepPos = GetAgentPosFromState(sheepId)
getWolfPos = GetAgentPosFromState(wolfId)
wolfSpeed = 1
wolfPolicy = HeatSeekingContinuousDeterministicPolicy(
getWolfPos, getSheepPos, wolfSpeed)
# wolfPolicy = lambda state: (0, 0)
xBoundary = (0, 20)
yBoundary = (0, 20)
stayWithinBoundary = StayWithinBoundary(xBoundary, yBoundary)
physicalTransition = TransitForNoPhysics(getIntendedNextState,
stayWithinBoundary)
transit = TransitWithSingleWolf(physicalTransition, wolfPolicy)
sheepAliveBonus = 1 / maxTimeStep
sheepTerminalPenalty = 20
killzoneRadius = 1
isTerminal = IsTerminal(getWolfPos, getSheepPos, killzoneRadius)
getBoundaryPunishment = GetBoundaryPunishment(xBoundary,
yBoundary,
sheepIndex=0,
punishmentVal=10)
rewardSheep = RewardFunctionCompete(sheepAliveBonus, sheepTerminalPenalty,
isTerminal)
getReward = RewardSheepWithBoundaryHeuristics(rewardSheep,
getIntendedNextState,
getBoundaryPunishment,
getSheepPos)
sampleOneStep = SampleOneStep(transit, getReward)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer)
# reset = Reset(xBoundary, yBoundary, numAgents)
# reset = lambda: np.array([10, 3, 15, 8]) #all [-1, -1] action
# reset = lambda: np.array([15, 8, 10, 3]) # all [1. 1.]
# reset = lambda: np.array([15, 10, 10, 10])
reset = lambda: np.array([10, 10, 15, 5])
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep, isTerminal)
ddpg = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = ddpg(replayBuffer)
trainedActorModel, trainedCriticModel = trainModels.getTrainedModels()
modelIndex = 0
actorFixedParam = {'actorModel': modelIndex}
criticFixedParam = {'criticModel': modelIndex}
parameters = {
'wolfSpeed': wolfSpeed,
'dimension': actionDim,
'maxEpisode': maxEpisode,
'maxTimeStep': maxTimeStep,
'minibatchSize': minibatchSize,
'gamma': gamma,
'learningRateActor': learningRateActor,
'learningRateCritic': learningRateCritic
}
modelSaveDirectory = "../trainedDDPGModels"
modelSaveExtension = '.ckpt'
getActorSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension,
actorFixedParam)
getCriticSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension,
criticFixedParam)
savePathActor = getActorSavePath(parameters)
savePathCritic = getCriticSavePath(parameters)
with actorModel.as_default():
saveVariables(trainedActorModel, savePathActor)
with criticModel.as_default():
saveVariables(trainedCriticModel, savePathCritic)
plotResult = True
if plotResult:
plt.plot(list(range(maxEpisode)), meanRewardList)
plt.show()
def __call__(self, df):
noiseVariance = df.index.get_level_values('noiseInitVariance')[0]
memorySize = df.index.get_level_values('memorySize')[0]
buildActorModel = BuildActorModel(self.fixedParameters['stateDim'],
self.fixedParameters['actionDim'],
self.fixedParameters['actionBound'])
actorWriter, actorModel = buildActorModel(
self.fixedParameters['actorLayerWidths'])
buildCriticModel = BuildCriticModel(self.fixedParameters['stateDim'],
self.fixedParameters['actionDim'])
criticWriter, criticModel = buildCriticModel(
self.fixedParameters['criticLayerWidths'])
trainCriticBySASRQ = TrainCriticBySASRQ(
self.fixedParameters['learningRateCritic'],
self.fixedParameters['gamma'], criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(
self.fixedParameters['learningRateActor'], actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
updateParameters = UpdateParameters(
self.fixedParameters['paramUpdateInterval'],
self.fixedParameters['tau'])
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor,
trainCritic, actorModel, criticModel)
getNoise = GetExponentialDecayGaussNoise(
noiseVariance, self.fixedParameters['varianceDiscount'],
self.fixedParameters['noiseDecayStartStep'])
actOneStepWithNoise = ActDDPGOneStep(
self.fixedParameters['actionLow'],
self.fixedParameters['actionHigh'], actByPolicyTrain, actorModel,
getNoise)
sampleFromMemory = SampleFromMemory(self.fixedParameters['batchSize'])
learnFromBuffer = LearnFromBuffer(
self.fixedParameters['learningStartStep'], sampleFromMemory,
trainModels)
transit = TransitGymPendulum()
getReward = RewardGymPendulum(angle_normalize)
sampleOneStep = SampleOneStep(transit, getReward)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer, observe)
reset = ResetGymPendulum(seed)
runEpisode = RunEpisode(reset, runDDPGTimeStep,
self.fixedParameters['maxRunSteps'],
isTerminalGymPendulum)
ddpg = RunAlgorithm(runEpisode, self.fixedParameters['maxEpisode'])
replayBuffer = deque(maxlen=int(memorySize))
meanRewardList, trajectory = ddpg(replayBuffer)
trainedActorModel, trainedCriticModel = trainModels.getTrainedModels()
timeStep = list(range(len(meanRewardList)))
resultSe = pd.Series(
{time: reward
for time, reward in zip(timeStep, meanRewardList)})
if self.saveModel:
actorParameters = {
'ActorMemorySize': memorySize,
'NoiseVariance': noiseVariance
}
criticParameters = {
'CriticMemorySize': memorySize,
'NoiseVariance': noiseVariance
}
actorPath = self.getSavePath(actorParameters)
criticPath = self.getSavePath(criticParameters)
with trainedActorModel.as_default():
saveVariables(trainedActorModel, actorPath)
with trainedCriticModel.as_default():
saveVariables(trainedCriticModel, criticPath)
return resultSe
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
buildCriticModel = BuildCriticModel(stateDim, actionDim)
criticLayerWidths = [30]
criticWriter, criticModel = buildCriticModel(criticLayerWidths)
trainCriticBySASRQ = TrainCriticBySASRQ(learningRateCritic, gamma,
criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(learningRateActor,
actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
paramUpdateInterval = 1
updateParameters = UpdateParameters(paramUpdateInterval, tau)
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor, trainCritic,
actorModel, criticModel)
noiseInitVariance = 3
varianceDiscount = .9995
noiseDecayStartStep = bufferSize
getNoise = GetExponentialDecayGaussNoise(noiseInitVariance,
varianceDiscount,
noiseDecayStartStep)
actOneStepWithNoise = ActDDPGOneStep(actionLow, actionHigh,
actByPolicyTrain, actorModel,
getNoise)
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
sampleOneStep = SampleOneStepUsingGym(env)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer)
reset = lambda: env.reset()
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep,
isTerminalGymPendulum)
ddpg = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = ddpg(replayBuffer)
trainedActorModel, trainedCriticModel = trainModels.getTrainedModels()
env.close()
plotResult = True
if plotResult:
plt.plot(list(range(maxEpisode)), meanRewardList)
plt.show()
def main():
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
buildActorModel = BuildActorModel(stateDim, actionDim, actionBound)
actorLayerWidths = [30]
actorWriter, actorModel = buildActorModel(actorLayerWidths)
buildCriticModel = BuildCriticModel(stateDim, actionDim)
criticLayerWidths = [30]
criticWriter, criticModel = buildCriticModel(criticLayerWidths)
trainCriticBySASRQ = TrainCriticBySASRQ(learningRateCritic, gamma,
criticWriter)
trainCritic = TrainCritic(actByPolicyTarget, evaluateCriticTarget,
trainCriticBySASRQ)
trainActorFromGradients = TrainActorFromGradients(learningRateActor,
actorWriter)
trainActorOneStep = TrainActorOneStep(actByPolicyTrain,
trainActorFromGradients,
getActionGradients)
trainActor = TrainActor(trainActorOneStep)
paramUpdateInterval = 1
updateParameters = UpdateParameters(paramUpdateInterval, tau)
modelList = [actorModel, criticModel]
actorModel, criticModel = resetTargetParamToTrainParam(modelList)
trainModels = TrainDDPGModels(updateParameters, trainActor, trainCritic,
actorModel, criticModel)
noiseInitVariance = 1 # control exploration
varianceDiscount = .99995
noiseDecayStartStep = bufferSize
minVar = .1
getNoise = GetExponentialDecayGaussNoise(noiseInitVariance,
varianceDiscount,
noiseDecayStartStep, minVar)
actOneStepWithNoise = ActDDPGOneStep(actionLow, actionHigh,
actByPolicyTrain, actorModel,
getNoise)
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
transit = TransitGymMountCarContinuous()
isTerminal = IsTerminalMountCarContin()
getReward = RewardMountCarContin(isTerminal)
sampleOneStep = SampleOneStep(transit, getReward)
runDDPGTimeStep = RunTimeStep(actOneStepWithNoise, sampleOneStep,
learnFromBuffer)
resetLow = -1
resetHigh = 0.4
reset = ResetMountCarContin(seed=None)
runEpisode = RunEpisode(reset, runDDPGTimeStep, maxTimeStep, isTerminal)
ddpg = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = ddpg(replayBuffer)
trainedActorModel, trainedCriticModel = trainModels.getTrainedModels()
# save Model
modelIndex = 0
actorFixedParam = {'actorModel': modelIndex}
criticFixedParam = {'criticModel': modelIndex}
parameters = {
'env': ENV_NAME,
'Eps': maxEpisode,
'timeStep': maxTimeStep,
'batch': minibatchSize,
'gam': gamma,
'lrActor': learningRateActor,
'lrCritic': learningRateCritic,
'noiseVar': noiseInitVariance,
'varDiscout': varianceDiscount,
'resetLow': resetLow,
'High': resetHigh
}
modelSaveDirectory = "../trainedDDPGModels"
modelSaveExtension = '.ckpt'
getActorSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension,
actorFixedParam)
getCriticSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension,
criticFixedParam)
savePathActor = getActorSavePath(parameters)
savePathCritic = getCriticSavePath(parameters)
with actorModel.as_default():
saveVariables(trainedActorModel, savePathActor)
with criticModel.as_default():
saveVariables(trainedCriticModel, savePathCritic)
dirName = os.path.dirname(__file__)
trajectoryPath = os.path.join(dirName, '..', 'trajectory',
'mountCarTrajectoryOriginalReset1.pickle')
saveToPickle(trajectory, trajectoryPath)
# plots& plot
showDemo = False
if showDemo:
visualize = VisualizeMountCarContin()
visualize(trajectory)
plotResult = True
if plotResult:
plt.plot(list(range(maxEpisode)), meanRewardList)
plt.show()