def objective(args):
NUM_TESTS_FOR_NOISE = 1
env = gym.make('LunarLander-v2')
learningRate = args
# numIntermediateLayers = int(numIntermediateLayers)
# intermediateLayerSize = int(intermediateLayerSize)
# finalLayerSize = int(finalLayerSize)
# layers = []
# for i in range(numIntermediateLayers):
# layers.append(intermediateLayerSize)
# layers.append(finalLayerSize)
# print("Layers: ",layers)
# print("Priority: ",priorityExponent)
# print("LR: ",learningRate)
totalResult = 0
for i in range(NUM_TESTS_FOR_NOISE):
sess = tf.Session()
a = Agent(
sess=sess,
env=env,
numAvailableActions=4,
numObservations=8,
rewardsMovingAverageSampleLength=20,
gamma=1,
nStepUpdate=1,
includeIntermediatePairs=False,
maxRunningMinutes=30,
# test parameters
episodesPerTest=1,
numTestPeriods=40000,
numTestsPerTestPeriod=30,
episodeStepLimit=1024,
intermediateTests=False,
render=False,
showGraph=False,
# hyperparameters
valueMin=-400.0,
valueMax=300.0,
numAtoms=14,
maxMemoryLength=100000,
batchSize=256,
networkSize=[128, 128, 256],
learningRate=learningRate,
priorityExponent=0,
epsilonInitial = 2,
epsilonDecay = .9987,
minFramesForTraining = 2048,
noisyLayers = False,
maxGradientNorm = 4,
minExploration = .15,
)
testResults = np.array(a.execute())
performance = np.mean(testResults[np.argpartition(-testResults,range(4))[:4]])
totalResult = totalResult + performance
print(str(learningRate)+","+str(performance))
return -totalResult
# agentName="agent_842763505",
# hyperparameters
rewardScaling=rewardScaling,
nStepReturns=1,
maxMemoryLength=int(1e6),
batchSize=64,
learningRate=6.25e-4,
priorityExponent=0,
epsilonInitial=1,
epsilonDecay=.999975,
minExploration=.01,
maxExploration=1.0,
minFramesForTraining=2048,
maxGradientNorm=5,
preNetworkSize=[128, 128],
postNetworkSize=[256],
numQuantiles=8,
embeddingDimension=16,
kappa=1.0,
trainingIterations=3,
tau=0.001)
performance = a.execute()[0]
cur = db.cursor()
cur.execute(
"insert into experiments (label, x1, x2, x3, x4, y) values ('{0}', '{1}', '{2}', '{3}', '{4}', '{5}')"
.format(experimentName, rewardScaling, 0, 0, 0, performance))
db.commit()
cur.close()
db.close()
intermediateTests=False,
render=False,
showGraph=True,
saveModel=True,
loadModel=False,
disableRandomActions=False,
disableTraining=False,
# agentName="agent_223120799",
# hyperparameters
rewardScaling=pow(10, -.75),
nStepReturns=1,
maxMemoryLength=int(1e6),
batchSize=64,
learningRate=6.25e-4,
priorityExponent=0,
epsilonInitial=1,
epsilonDecay=.999,
minExploration=.01,
maxExploration=1.0,
minFramesForTraining=2048,
maxGradientNorm=5,
preNetworkSize=[256, 256],
postNetworkSize=[512],
numQuantiles=32,
embeddingDimension=64,
kappa=1.0,
trainingIterations=3,
tau=0.001)
testResults = a.execute()
