# print 'added feature'
self.addNewWeight()
addedFeature = True
self.features_num += 1
self.logger.log('Added feature. \t %d total feats' % self.features_num)
else:
break
return addedFeature
def featureType(self):
return float
if __name__ == '__main__':
STDOUT_FILE = 'out.txt'
JOB_ID = 1
OUT_PATH = 'Results/Temp'
# logger = Logger('%s/%d-%s'%(OUT_PATH,JOB_ID,STDOUT_FILE))
logger = Logger()
discovery_threshold = 1
domain = GridWorld()
rep = BEBF(domain,logger,debug=1,batchThreshold = 10 ** -5)
rep.theta = arange(rep.features_num*domain.actions_num)*10
print 'initial features'
print rep.features_num,'---',rep.features
s = domain.s0()
a = domain.possibleActions(s)
a = a[0]
r,ns,terminal = domain.step(s, a)
print 'step 2 r,ns',r,ns
r = v / v.sum()
assert not np.any(np.isnan(r))
return r
if __name__ == "__main__":
# Finite differences check of dlogpi
from Domains import GridWorld
from Representations import Tabular
from scipy.optimize import check_grad, approx_fprime
MAZE = "./Domains/GridWorldMaps/4x5.txt"
NOISE = 0.3
logger = Logger()
domain = GridWorld(MAZE, noise=NOISE, logger=logger)
representation = Tabular(logger=logger, domain=domain, discretization=20)
policy = GibbsPolicy(representation=representation, logger=logger)
def f(theta, s, a):
policy.representation.theta = theta
return np.log(policy.prob(s, a))
def df(theta, s, a):
policy.representation.theta = theta
return policy.dlogpi(s, a)
def df_approx(theta, s, a):
return approx_fprime(theta, f, 1e-10, s, a)
thetas = np.random.rand(10, len(representation.theta))
