def perform_gradient_descent(self,chromosome): from pybrain.optimization import ExactNES, OriginalNES inp = numpy.array([v for v in chromosome]) if self.num_eval != 0: #bf = BoundsSafeFunction(self.func,self.bounds) l = ExactNES(objF, inp[:],rangemins=self.mins,rangemaxs=self.maxs,learningRate=0.01,initCovariances=numpy.eye(len(bounds))*0.1) l.minimize = True l.maxEvaluations = self.num_eval #l.rangemins = self.mins #l.rangemaxs = self.maxs (new_K,success) = l.learn() for i in xrange(0,len(chromosome)): chromosome[i] = new_K[i] score = objF(numpy.array(new_K)) return score
def fit(self, sast=None, r=None):
if sast is not None:
next_states_idx = self.state_dim + self.action_dim
self._sa = sast[:, :next_states_idx]
self._snext = sast[:, next_states_idx:-1]
self._absorbing = sast[:, -1]
if r is not None:
self._r = r
old_theta = self._estimator._regressor.theta
self._optimizer = ExactNES(self._fitness,
self._get_rho(),
minimize=True,
batchSize=100)
rho, score = self._optimizer.learn()
self._estimator._regressor.theta = self._f(rho)
self._iteration += 1
return (self._estimator._regressor.theta,
np.sum(self._estimator._regressor.theta - old_theta)**2)
def fit(self, sast, r):
"""
Perform a run of PBO using input data sast and r.
Note that if the dataset does not change between iterations, you can
provide None inputs after the first iteration.
Args:
sast (numpy.array, None): the input in the dataset
r (numpy.array, None): the output in the dataset
**kwargs: additional parameters to be provided to the fit function
of the estimator
Returns:
the history of the parameters used to update the q regressor
"""
self.iteration_best_rho_value = np.inf
next_states_idx = self.state_dim + self.action_dim
self._sa = sast[:, :next_states_idx]
self._snext = sast[:, next_states_idx:-1]
self._absorbing = sast[:, -1]
self._r = r
optimizer = ExactNES(self._fitness,
self._get_rho(),
minimize=True,
batchSize=self._batch_size,
learningRate=self._learning_rate,
maxLearningSteps=self._learning_steps - 1,
importanceMixing=False,
maxEvaluations=None)
optimizer.listener = self.my_listener
optimizer.learn()
self._q_weights_list.append(self._get_q_weights())
return self._q_weights_list
from pybrain.optimization import ExactNES
from pybrain.rl.experiments import EpisodicExperiment
batch = 2 #number of samples per learning step
prnts = 100 #number of learning steps after results are printed
epis = 4000 / batch / prnts #number of roleouts
numbExp = 10 #number of experiments
et = ExTools(batch, prnts) #tool for printing and plotting
for runs in range(numbExp):
# create environment
env = CartPoleEnvironment()
# create task
task = BalanceTask(env, 200, desiredValue=None)
# create controller network
net = buildNetwork(4, 1, bias=False)
# create agent with controller and learner (and its options)
agent = OptimizationAgent(net, ExactNES(storeAllEvaluations=True))
et.agent = agent
# create the experiment
experiment = EpisodicExperiment(task, agent)
#Do the experiment
for updates in range(epis):
for i in range(prnts):
experiment.doEpisodes(batch)
print "Epsilon : ", agent.learner.sigma
et.printResults((agent.learner._allEvaluations)[-50:-1], runs, updates)
et.addExps()
et.showExps()
