def train(self, episodes, maxSteps):
avgReward = 0
# set up environment and task
self.env = InfoMaxEnv(self.objectNames, self.actionNames,
self.numCategories)
self.task = InfoMaxTask(self.env, maxSteps=maxSteps, \
do_decay_beliefs = True, uniformInitialBeliefs = True)
# create neural net and learning agent
self.params = buildNetwork(self.task.outdim, self.task.indim, \
bias=True, outclass=SoftmaxLayer)
if self._PGPE:
self.agent = OptimizationAgent(self.params,
PGPE(minimize=False, verbose=False))
elif self._CMAES:
self.agent = OptimizationAgent(
self.params, CMAES(minimize=False, verbose=False))
# init and perform experiment
exp = EpisodicExperiment(self.task, self.agent)
for i in range(episodes):
exp.doEpisodes(1)
avgReward += self.task.getTotalReward()
print "reward episode ", i, self.task.getTotalReward()
# print initial info
print "\naverage reward over training = ", avgReward / episodes
# save trained network
self._saveWeights()
def run(self, maxSteps):
self.env = InfoMaxEnv(self.objectNames, self.actionNames,
self.numCategories)
self.task = InfoMaxTask(self.env, maxSteps=maxSteps, \
do_decay_beliefs = True, uniformInitialBeliefs = True)
self.task.reset()
# load network if we're just running, not training
self.params = pickle.load(open('infomaxNet.pkl'))
self.params.sorted = False
self.params.sortModules()
print "\n"
while not self.task.isFinished():
# get initial observation of environment
obs_pre = self.task.getObservation()
print "State pre"
#print self.task.showBeliefs() # use formatted print beliefs function
print self.task.getObservation()
# send observation to net for an action vector
action = self.params.activate(obs_pre)
# send action vector to robot
self.task.performAction(action)
print "State post"
#print self.task.showBeliefs()
print self.task.getObservation()
# calculate and show reward
print "reward", self.task.getReward()
print "\n"
print "total reward =", self.task.getTotalReward()
print "\n"
