saveNet = False
saveName = "grasp.wgt"
numbExp = 1 #number of experiments
for runs in range(numbExp):
# create environment
#Options: XML-Model, Bool(OpenGL), Bool(Realtime simu. while client is connected), ServerIP(default:localhost), Port(default:21560)
env = CCRLEnvironment()
# create task
task = CCRLGlasVarTask(env)
# create controller network
net = buildNetwork(len(task.getObservation()),
hiddenUnits,
env.actLen,
outclass=TanhLayer)
# create agent with controller and learner
agent = FiniteDifferenceAgent(net, SPLA())
# learning options
agent.learner.gd.alpha = 0.2 #step size of \mu adaption
agent.learner.gdSig.alpha = 0.085 #step size of \sigma adaption
agent.learner.gd.momentum = 0.0
#Loading weights
if loadNet:
agent.learner.original = loadWeights("grasp.wgt")
agent.learner.gd.init(agent.learner.original)
agent.learner.epsilon = 0.2
agent.learner.initSigmas()
batch = 2 #number of samples per gradient estimate
#create experiment
experiment = EpisodicExperiment(task, agent)
filepointer = file(filename, 'w+')
dump(w, filepointer)
filepointer.close()
numbExp = 1 #number of experiments
for runs in range(numbExp):
# create environment
#Options: Bool(OpenGL), Bool(Realtime simu. while client is connected), ServerIP(default:localhost), Port(default:21560)
env = ShipSteeringEnvironment(False)
# create task
task = GoNorthwardTask(env, maxsteps=500)
# create controller network
net = buildNetwork(task.outdim, task.indim, outclass=TanhLayer)
# create agent with controller and learner
agent = FiniteDifferenceAgent(net, SimpleSPSA())
# learning options
agent.learner.gd.alpha = 0.5 #step size of parameter adaption
agent.learner.gamma = 0.9993 #exploration decay factor
agent.learner.gd.momentum = 0.0
batch = 2 #number of samples per gradient estimate (Symetric sampling needs odd number of samples per estimate!)
#create experiment
experiment = EpisodicExperiment(task, agent)
prnts = 10 #frequency of console output
epis = 10000 / batch / prnts
#actual roll outs
#filename="dataSPLA08NoRew"+repr(int(random.random()*1000000.0))+".dat"
#wf = open(filename, 'wb')
for updates in range(epis):
for i in range(prnts):
