def launchDDPGForSpecificTargetSizeAndSpeceficBeginning(
sizeOfTarget, rs, point):
'''
Run cmaes for a specific target sizeCMAES
Input: -sizeOfTarget, size of the target, float
-setuFile, file of setup, string
-save, for saving result, bool
'''
pos = point[0]
x = point[1][0]
y = point[1][1]
print("Starting the DDPGor target " + str(sizeOfTarget) + " for point " +
str(pos) + " !")
foldername = rs.OPTIpath + str(sizeOfTarget) + "/" + str(pos) + "/"
actor = simple_actor_network(rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate)
env = DDPGEnv(rs,
sizeOfTarget,
"Best.theta",
actor=actor,
saveDir=foldername)
env.setOnePointController(x, y)
ddpg = DDPG(env, actor=actor)
ddpg.M_episodes(rs.maxIterDDPG, train=False)
print("End of optimization for target " + str(sizeOfTarget) +
" for point " + str(pos) + " !")
def launchDDPGForSpecificTargetSizeAndSpeceficBeginning(sizeOfTarget, rs, point):
"""
Run cmaes for a specific target sizeCMAES
Input: -sizeOfTarget, size of the target, float
-setuFile, file of setup, string
-save, for saving result, bool
"""
pos = point[0]
x = point[1][0]
y = point[1][1]
print("Starting the DDPGor target " + str(sizeOfTarget) + " for point " + str(pos) + " !")
foldername = rs.OPTIpath + str(sizeOfTarget) + "/" + str(pos) + "/"
actor = simple_actor_network(
rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate,
)
env = DDPGEnv(rs, sizeOfTarget, "Best.theta", actor=actor, saveDir=foldername)
env.setOnePointController(x, y)
ddpg = DDPG(env, actor=actor)
ddpg.M_episodes(rs.maxIterDDPG, train=False)
print("End of optimization for target " + str(sizeOfTarget) + " for point " + str(pos) + " !")
def launchDDPGForSpecificTargetSize(sizeOfTarget, rs):
actor = simple_actor_network(rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate)
env = DDPGEnv(rs, sizeOfTarget, rs.thetaFile, actor=actor)
ddpg = DDPG(env, actor=actor)
ddpg.M_episodes(rs.maxIterDDPG, train=False)
def generateFromDDPG(repeat, rs, thetaFile, saveDir='Data'):
for el in rs.sizeOfTarget:
c = Chrono()
actor = simple_actor_network(rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate)
env = DDPGEnv(rs, el, rs.thetaFile, actor=actor, saveDir=saveDir)
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile + ".theta"
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
os.system("rm " + saveName + "Log/*.log 2>/dev/null")
parameters = np.loadtxt(thetaName)
actor.load_parameters(parameters)
cost, time = env.saveAllTraj(repeat)
c.stop()
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", saveName)
print("DDPG:End of generation")
def generateFromDDPG(repeat, rs, thetaFile, saveDir="Data"):
for el in rs.sizeOfTarget:
c = Chrono()
actor = simple_actor_network(
rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate,
)
env = DDPGEnv(rs, el, rs.thetaFile, actor=actor, saveDir=saveDir)
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile + ".theta"
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
os.system("rm " + saveName + "Log/*.log 2>/dev/null")
parameters = np.loadtxt(thetaName)
actor.load_parameters(parameters)
cost, time = env.saveAllTraj(repeat)
c.stop()
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", saveName)
print("DDPG:End of generation")