class InfoMaxAgent():
def __init__(self, option):
# examine command-line argument for learning algorithm
if option[1] == "PGPE":
self._PGPE = True; self._CMAES = False
elif option[1] == "CMAES":
self._PGPE = False; self._CMAES = True
else: self._PGPE = True
# tuples are name, category
self.objectNames = [('pink_glass',0), # 0
('german_ball',1), # 1
('blue_cup',2), # 2
('blue_spiky_ball',3), # 3
('screw_box',4), # 4
('wire_spool',5), # 5
('sqeaky_ball',6), # 6
('duck_tape_roll',7), # 7
('ace_terminals',8), # 8
('chalkboard_eraser',9), # 9
]
#self.actionNames = ["pick up", "drop", "push", "squeeze"]
self.actionNames = ['grasp', # 0
'lift', # 1
'drop', # 2
'shake_roll', # 3
'place', # 4
'push', # 5
'shake_pitch', # 6
]
# objects and their categories
self.numCategories = 10
#self.actionNames = ["pick up", "drop", "push", "squeeze", "move left", "move right", "reset"]
# run trained network in our environment
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"
# train a new network with PGPE or CMAES
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()
# save and pickle trained weights for later reuse
def _saveWeights(self):
# import weights into network and save network
if self._PGPE:
for i in range(len(self.params.params)):
self.params.params[i] = self.agent.learner.current[i]
pickle.dump(self.params, open('infomaxNet.pkl','w'))
elif self._CMAES:
################ following code came from WWInfoMaxCMAES.py script from ICDL 2010 paper
arz = randn(self.agent.learner.numParameters, self.agent.learner.batchSize)
arx = tile(self.agent.learner.center.reshape(self.agent.learner.numParameters, 1),\
(1, self.agent.learner.batchSize)) + \
self.agent.learner.stepSize * dot(dot(self.agent.learner.B, self.agent.learner.D), arz)
# Go through the parameters and pick the current best
arfitness = zeros(self.agent.learner.batchSize)
for k in xrange(self.agent.learner.batchSize):
self.agent.learner.wrappingEvaluable._setParameters(arx[:, k]);
arfitness[k] = self.agent.learner._BlackBoxOptimizer__evaluator\
(self.agent.learner.wrappingEvaluable)
# Sort by fitness and compute weighted mean into center
tmp = sorted(map(lambda (x, y): (y, x), enumerate(ravel(arfitness))))
arfitness = array(map(lambda x: x[0], tmp))
arindex = array(map(lambda x: int(x[1]), tmp))
arz = arz[:, arindex]
curparams = arx[:, arindex[0]];
# update network weights with selected parameters
for i in range(len(self.params.params)):
self.params.params[i] = curparams[i]
# save trained network
pickle.dump(self.params, open('infomaxNet.pkl','w'))
class InfoMaxAgent():
def __init__(self, option):
# examine command-line argument for learning algorithm
if option[1] == "PGPE":
self._PGPE = True
self._CMAES = False
elif option[1] == "CMAES":
self._PGPE = False
self._CMAES = True
else:
self._PGPE = True
# tuples are name, category
self.objectNames = [
('pink_glass', 0), # 0
('german_ball', 1), # 1
('blue_cup', 2), # 2
('blue_spiky_ball', 3), # 3
('screw_box', 4), # 4
('wire_spool', 5), # 5
('sqeaky_ball', 6), # 6
('duck_tape_roll', 7), # 7
('ace_terminals', 8), # 8
('chalkboard_eraser', 9), # 9
]
#self.actionNames = ["pick up", "drop", "push", "squeeze"]
self.actionNames = [
'grasp', # 0
'lift', # 1
'drop', # 2
'shake_roll', # 3
'place', # 4
'push', # 5
'shake_pitch', # 6
]
# objects and their categories
self.numCategories = 10
#self.actionNames = ["pick up", "drop", "push", "squeeze", "move left", "move right", "reset"]
# run trained network in our environment
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"
# train a new network with PGPE or CMAES
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()
# save and pickle trained weights for later reuse
def _saveWeights(self):
# import weights into network and save network
if self._PGPE:
for i in range(len(self.params.params)):
self.params.params[i] = self.agent.learner.current[i]
pickle.dump(self.params, open('infomaxNet.pkl', 'w'))
elif self._CMAES:
################ following code came from WWInfoMaxCMAES.py script from ICDL 2010 paper
arz = randn(self.agent.learner.numParameters,
self.agent.learner.batchSize)
arx = tile(self.agent.learner.center.reshape(self.agent.learner.numParameters, 1),\
(1, self.agent.learner.batchSize)) + \
self.agent.learner.stepSize * dot(dot(self.agent.learner.B, self.agent.learner.D), arz)
# Go through the parameters and pick the current best
arfitness = zeros(self.agent.learner.batchSize)
for k in xrange(self.agent.learner.batchSize):
self.agent.learner.wrappingEvaluable._setParameters(arx[:, k])
arfitness[k] = self.agent.learner._BlackBoxOptimizer__evaluator\
(self.agent.learner.wrappingEvaluable)
# Sort by fitness and compute weighted mean into center
tmp = sorted(
map(lambda (x, y): (y, x), enumerate(ravel(arfitness))))
arfitness = array(map(lambda x: x[0], tmp))
arindex = array(map(lambda x: int(x[1]), tmp))
arz = arz[:, arindex]
curparams = arx[:, arindex[0]]
# update network weights with selected parameters
for i in range(len(self.params.params)):
self.params.params[i] = curparams[i]
# save trained network
pickle.dump(self.params, open('infomaxNet.pkl', 'w'))