def __call__(self, df):
learningRate = df.index.get_level_values('learningRate')[0]
buffersize = df.index.get_level_values('buffersize')[0]
visualize = visualizeCartpole()
reset = resetCartpole()
transition = CartPoletransition()
rewardcart = CartPoleReward()
isterminal = isTerminal()
replaybuffer = deque(maxlen=int(buffersize))
trajectory = []
totalrewards = []
averagerewards = []
buildmodel = BuildModel(self.fixedParameters['stateDim'],self.fixedParameters['actionDim'])
Writer,DQNmodel = buildmodel(self.fixedParameters['numberlayers'])
replaceParameters = ReplaceParameters(self.fixedParameters['replaceiter'])
trainModel = TrainModel(learningRate, self.fixedParameters['gamma'],Writer)
trainDQNmodel = TrainDQNmodel(replaceParameters, trainModel, DQNmodel)
learn = Learn(buffersize,self.fixedParameters['batchsize'],trainDQNmodel,self.fixedParameters['actionDim'])
runepsilon = self.fixedParameters['initepsilon']
for episode in range(self.fixedParameters['maxEpisode']):
state = reset()
rewards = 0
while True:
visualize(state)
runepsilon = epsilonDec(runepsilon,self.fixedParameters['minepsilon'],self.fixedParameters['epsilondec'])
action = learn.Getaction(DQNmodel,runepsilon,state)
nextstate=transition(state, action)
done = isterminal(nextstate)
reward = rewardcart(state,action,nextstate,done)
trajectory.append((state, action, reward, nextstate))
learn.ReplayMemory(replaybuffer,state, action, reward, nextstate,done)
rewards += reward
state = nextstate
if done:
totalrewards.append(rewards)
print('episode: ',episode,'reward:',rewards,'epsilon:',runepsilon)
break
averagerewards.append(np.mean(totalrewards))
print('episode:',episode,'meanreward:',np.mean(totalrewards))
timeStep = list(range(len(averagerewards)))
resultSe = pd.Series({time: reward for time, reward in zip(timeStep, averagerewards)})
if self.saveModel:
Parameters = {'learningRate': learningRate, 'buffersize': buffersize }
modelPath = self.getSavePath(Parameters)
with DQNmodel.as_default():
saveVariables(DQNmodel, modelPath)
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 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
replaybuffer = deque(maxlen=buffersize)
paramUpdateFrequency = 1
totalrewards = []
meanreward = []
totalreward = []
buildActorModel = BuildActorModel(statedim, actiondim, actionBound)
actorWriter, actorModel = buildActorModel(actornumberlayers)
buildCriticModel = BuildCriticModel(statedim, actiondim)
criticWriter, criticModel = buildCriticModel(criticnumberlayers)
trainCritic = TrainCritic(criticlearningRate, gamma, criticWriter)
trainActor = TrainActor(actorlearningRate, actorWriter)
updateParameters = UpdateParameters(tau,paramUpdateFrequency)
actorModel= ReplaceParameters(actorModel)
criticModel= ReplaceParameters(criticModel)
trainddpgModels = TrainDDPGModels(updateParameters, trainActor, trainCritic, actorModel, criticModel)
getnoise = GetNoise(noiseDecay,minVar,noiseDacayStep)
getnoiseaction = GetNoiseAction(actorModel,actionLow, actionHigh)
learn = Learn(buffersize,batchsize,trainddpgModels,actiondim)
runtime = 0
trajectory = []
noisevar = initnoisevar
for episode in range(EPISODE):
state = env.reset()
rewards = 0
for i in range(maxTimeStep):
env.render()
noise,noisevar = getnoise(runtime,noisevar)
noiseaction = getnoiseaction(state,noise)
nextstate,reward,done,info = env.step(noiseaction)
learn(replaybuffer,state, noiseaction, nextstate,reward)
trajectory.append((state, noiseaction, nextstate,reward))
rewards += reward
state = nextstate
runtime += 1
print(actionHigh,actionLow)
if i == maxTimeStep-1:
totalrewards.append(rewards)
totalreward.append(rewards)
print('episode: ',episode,'reward:',rewards, 'noisevar',noisevar)
if episode % 100 == 0:
meanreward.append(np.mean(totalreward))
print('episode: ',episode,'meanreward:',np.mean(totalreward))
totalreward = []
plt.plot(range(EPISODE),totalrewards)
plt.xlabel('episode')
plt.ylabel('rewards')
plt.show()
# save Model
modelIndex = 0
actorFixedParam = {'actorModel': modelIndex}
criticFixedParam = {'criticModel': modelIndex}
parameters = {'env': Env_name, 'Eps': EPISODE, 'batchsize': batchsize,'buffersize': buffersize,'maxTimeStep':maxTimeStep,
'gamma': gamma, 'actorlearningRate': actorlearningRate, 'criticlearningRate': criticlearningRate,
'tau': tau, 'noiseDecay': noiseDecay, 'minVar': minVar, 'initnoisevar': initnoisevar}
modelSaveDirectory = "/path/to/logs/trainedDDPGModels"
modelSaveExtension = '.ckpt'
getSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension, actorFixedParam)
getSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension, criticFixedParam)
savePathDQN = getSavePath(parameters)
with actorModel.as_default():
saveVariables(actorModel, savePathDQN)
with criticModel.as_default():
saveVariables(criticModel, savePathDQN)
dirName = os.path.dirname(__file__)
trajectoryPath = os.path.join(dirName,'trajectory', 'HopperTrajectory.pickle')
saveToPickle(trajectory, trajectoryPath)
def __call__(self,env):
env = env_norm(env) if self.fixedParameters['normalizeEnv'] else env
actionHigh = env.action_space.high
actionLow = env.action_space.low
actionBound = (actionHigh - actionLow) / 2
stateDim = env.observation_space.shape[0]
actionDim = env.action_space.shape[0]
meanreward = []
trajectory = []
totalreward = []
totalrewards = []
episodereward = []
replaybuffer = deque(maxlen=int(self.fixedParameters['bufferSize']))
buildActorModel = BuildActorModel(stateDim, actionDim,actionBound ,
self.fixedParameters['actorHiddenLayersWeightInit'],self.fixedParameters['actorHiddenLayersBiasInit'],
self.fixedParameters['actorOutputWeightInit'], self.fixedParameters['actorOutputBiasInit'],self.fixedParameters['actorActivFunction'],self.fixedParameters['gradNormClipValue'],self.fixedParameters['normalizeEnv'])
actorModel = buildActorModel(self.fixedParameters['actorHiddenLayersWidths'])
buildCriticModel = BuildCriticModel(stateDim, actionDim,
self.fixedParameters['criticHiddenLayersWeightInit'],self.fixedParameters['criticHiddenLayersBiasInit'],
self.fixedParameters['criticOutputWeightInit'], self.fixedParameters['criticOutputBiasInit'],
self.fixedParameters['criticActivFunction'],self.fixedParameters['gradNormClipValue'],self.fixedParameters['normalizeEnv'])
criticModel = buildCriticModel(self.fixedParameters['criticHiddenLayersWidths'])
trainCritic = TrainCritic(self.fixedParameters['criticLR'], self.fixedParameters['gamma'])
trainActor = TrainActor(self.fixedParameters['actorLR'])
updateParameters = UpdateParameters(self.fixedParameters['tau'])
trainddpgModels = TrainDDPGModels(updateParameters, trainActor, trainCritic, actorModel,criticModel)
getnoise = GetNoise(self.fixedParameters['varianceDiscount'],self.fixedParameters['minVar'],self.fixedParameters['noiseDecayStartStep'],self.fixedParameters['noiseInitVariance'])
getnoiseaction = GetNoiseAction(actorModel,actionLow, actionHigh)
learn = Learn(self.fixedParameters['bufferSize'],self.fixedParameters['minibatchSize'],trainddpgModels)
actorModel= ReplaceParameters(actorModel)
criticModel= ReplaceParameters(criticModel)
state = env.reset()
replaybuffer = fillbuffer(3000,self.bufferfill,env,replaybuffer,state)
for episode in range(1,self.fixedParameters['maxEpisode']+1):
state = env.reset()
rewards = 0
for j in range(self.fixedParameters['maxTimeStep']):
env.render()
noise = getnoise(self.runstep)
noiseaction = getnoiseaction(state,noise)
nextstate,reward,done,info = env.step(noiseaction)
learn(replaybuffer,state, noiseaction, nextstate,reward)
trajectory.append((state, noiseaction, nextstate,reward))
rewards += reward
state = nextstate
self.runstep += 1
if j == self.fixedParameters['maxTimeStep']-1:
totalrewards.append(rewards)
totalreward.append(rewards)
print('episode: ',episode,'reward:',rewards,'runstep',self.runstep)
episodereward.append(np.mean(totalrewards))
print('epireward',np.mean(totalrewards))
if episode % 100 == 0:
meanreward.append(np.mean(totalreward))
print('episode: ',episode,'meanreward:',np.mean(totalreward))
totalreward = []
with actorModel.as_default():
saveVariables(actorModel, self.fixedParameters['modelSavePathMartin'])
with criticModel.as_default():
saveVariables(criticModel, self.fixedParameters['modelSavePathMartin'])
saveToPickle(meanreward, self.fixedParameters['rewardSavePathMartin'])
return episodereward
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 = 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)
def main():
env = MtCarDiscreteEnvSetup()
visualize = visualizeMtCarDiscrete()
reset = resetMtCarDiscrete(1234)
transition = MtCarDiscreteTransition()
rewardMtCar = MtCarDiscreteReward()
isterminal = MtCarDiscreteIsTerminal()
statesdim = env.observation_space.shape[0]
actiondim = env.action_space.n
replaybuffer = deque(maxlen=buffersize)
runepsilon = initepsilon
totalrewards = []
meanreward = []
trajectory = []
totalreward = []
buildmodel = BuildModel(statesdim, actiondim)
Writer, DQNmodel = buildmodel(numberlayers)
replaceParameters = ReplaceParameters(replaceiter)
trainModel = TrainModel(learningRate, gamma, Writer)
trainDQNmodel = TrainDQNmodel(replaceParameters, trainModel, DQNmodel)
learn = Learn(buffersize, batchsize, trainDQNmodel, actiondim)
for episode in range(EPISODE):
state = reset()
rewards = 0
runtime = 0
while True:
action = learn.Getaction(DQNmodel, runepsilon, state)
nextstate = transition(state, action)
done = isterminal(nextstate)
reward = rewardMtCar(state, action, nextstate, done)
learn.ReplayMemory(replaybuffer, state, action, reward, nextstate,
done)
trajectory.append((state, action, reward, nextstate))
rewards += reward
state = nextstate
runtime += 1
if runtime == 200:
totalrewards.append(rewards)
totalreward.append(rewards)
runtime = 0
print('episode: ', episode, 'reward:', rewards, 'epsilon:',
runepsilon)
break
if done:
totalrewards.append(rewards)
totalreward.append(rewards)
print('episode: ', episode, 'reward:', rewards, 'epsilon:',
runepsilon)
break
runepsilon = epsilonDec(runepsilon, minepsilon, epsilondec)
if episode % 100 == 0:
meanreward.append(np.mean(totalreward))
print('episode: ', episode, 'meanreward:', np.mean(totalreward))
totalreward = []
episode = 100 * (np.arange(len(meanreward)))
plt.plot(episode, meanreward)
plt.xlabel('episode')
plt.ylabel('rewards')
plt.ylim([-200, -50])
plt.show()
# save Model
modelIndex = 0
DQNFixedParam = {'DQNmodel': modelIndex}
parameters = {
'env': ENV_NAME,
'Eps': EPISODE,
'batch': batchsize,
'buffersize': buffersize,
'gam': gamma,
'learningRate': learningRate,
'replaceiter': replaceiter,
'epsilondec': epsilondec,
'minepsilon': minepsilon,
'initepsilon': initepsilon
}
modelSaveDirectory = "/path/to/logs/trainedDQNModels"
modelSaveExtension = '.ckpt'
getSavePath = GetSavePath(modelSaveDirectory, modelSaveExtension,
DQNFixedParam)
savePathDQN = getSavePath(parameters)
with DQNmodel.as_default():
saveVariables(DQNmodel, savePathDQN)
dirName = os.path.dirname(__file__)
trajectoryPath = os.path.join(dirName, 'trajectory',
'mountCarTrajectory.pickle')
saveToPickle(trajectory, trajectoryPath)