def testBufferSampleSize(self):
bufferSize = 5
buffer = getBuffer(bufferSize)
for i in range(5):
buffer.append((1, i))
minibatchSize = 2
sampleBuffer = SampleFromMemory(minibatchSize)
sample = sampleBuffer(buffer)
self.assertEqual(len(sample), minibatchSize)
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):
actionDim = df.index.get_level_values('actionDim')[0]
epsilonIncrease = df.index.get_level_values('epsilonIncrease')[0]
stateDim = env.observation_space.shape[0]
buildModel = BuildModel(stateDim, actionDim)
layersWidths = [30]
writer, model = buildModel(layersWidths)
learningRate = 0.001
gamma = 0.99
trainModelBySASRQ = TrainModelBySASRQ(learningRate, gamma, writer)
paramUpdateInterval = 300
updateParameters = UpdateParameters(paramUpdateInterval)
model = resetTargetParamToTrainParam([model])[0]
trainModels = TrainDQNModel(getTargetQValue, trainModelBySASRQ,
updateParameters, model)
epsilonMax = 0.9
epsilonMin = 0
bufferSize = 10000
decayStartStep = bufferSize
getEpsilon = GetEpsilon(epsilonMax, epsilonMin, epsilonIncrease,
decayStartStep)
actGreedyByModel = ActGreedyByModel(getTrainQValue, model)
actRandom = ActRandom(actionDim)
actByTrainNetEpsilonGreedy = ActByTrainNetEpsilonGreedy(
getEpsilon, actGreedyByModel, actRandom)
minibatchSize = 128
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize,
sampleFromMemory, trainModels)
processAction = ProcessDiscretePendulumAction(actionDim)
transit = TransitGymPendulum(processAction)
getReward = RewardGymPendulum(angle_normalize, processAction)
sampleOneStep = SampleOneStep(transit, getReward)
runDQNTimeStep = RunTimeStep(actByTrainNetEpsilonGreedy, sampleOneStep,
learnFromBuffer, observe)
reset = ResetGymPendulum(seed)
maxTimeStep = 200
runEpisode = RunEpisode(reset, runDQNTimeStep, maxTimeStep,
isTerminalGymPendulum)
maxEpisode = 400
dqn = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = dqn(replayBuffer)
timeStep = list(range(len(meanRewardList)))
resultSe = pd.Series(
{time: reward
for time, reward in zip(timeStep, meanRewardList)})
return resultSe
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 setUp(self):
minibatchSize = 32
self.sampleFromBuffer = SampleFromMemory(minibatchSize)
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()
def main():
stateDim = env.observation_space.shape[0]
actionDim = 7
buildModel = BuildModel(stateDim, actionDim)
layersWidths = [30]
writer, model = buildModel(layersWidths)
learningRate = 0.001
gamma = 0.99
trainModelBySASRQ = TrainModelBySASRQ(learningRate, gamma, writer)
paramUpdateInterval = 300
updateParameters = UpdateParameters(paramUpdateInterval)
model = resetTargetParamToTrainParam([model])[0]
trainModels = TrainDQNModel(getTargetQValue, trainModelBySASRQ, updateParameters, model)
epsilonMax = 0.9
epsilonIncrease = 0.0001
epsilonMin = 0
bufferSize = 10000
decayStartStep = bufferSize
getEpsilon = GetEpsilon(epsilonMax, epsilonMin, epsilonIncrease, decayStartStep)
actGreedyByModel = ActGreedyByModel(getTrainQValue, model)
actRandom = ActRandom(actionDim)
actByTrainNetEpsilonGreedy = ActByTrainNetEpsilonGreedy(getEpsilon, actGreedyByModel, actRandom)
minibatchSize = 128
learningStartBufferSize = minibatchSize
sampleFromMemory = SampleFromMemory(minibatchSize)
learnFromBuffer = LearnFromBuffer(learningStartBufferSize, sampleFromMemory, trainModels)
processAction = ProcessDiscretePendulumAction(actionDim)
transit = TransitGymPendulum(processAction)
getReward = RewardGymPendulum(angle_normalize, processAction)
sampleOneStep = SampleOneStep(transit, getReward)
runDQNTimeStep = RunTimeStep(actByTrainNetEpsilonGreedy, sampleOneStep, learnFromBuffer, observe)
reset = ResetGymPendulum(seed)
maxTimeStep = 200
runEpisode = RunEpisode(reset, runDQNTimeStep, maxTimeStep, isTerminalGymPendulum)
maxEpisode = 400
dqn = RunAlgorithm(runEpisode, maxEpisode)
replayBuffer = deque(maxlen=int(bufferSize))
meanRewardList, trajectory = dqn(replayBuffer)
trainedModel = trainModels.getTrainedModels()
# save Model
parameters = {'maxEpisode': maxEpisode, 'maxTimeStep': maxTimeStep, 'minibatchSize': minibatchSize, 'gamma': gamma,
'learningRate': learningRate, 'epsilonIncrease': epsilonIncrease , 'epsilonMin': epsilonMin}
modelSaveDirectory = "../trainedDQNModels"
modelSaveExtension = '.ckpt'
getSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension)
savePath = getSavePath(parameters)
with trainedModel.as_default():
saveVariables(trainedModel, savePath)
dirName = os.path.dirname(__file__)
trajectoryPath = os.path.join(dirName, '..', 'trajectory', 'pendulumDQNTrajectory.pickle')
saveToPickle(trajectory, trajectoryPath)
plotResult = True
if plotResult:
plt.plot(list(range(maxEpisode)), meanRewardList)
plt.show()
showDemo = False
if showDemo:
visualize = VisualizeGymPendulum()
visualize(trajectory)