def __init__(self,
mu0,
std0,
popsize,
std_decay=0.999,
min_std=0.01,
lr=1e-3,
lr_decay=0.9999,
min_lr=1e-2,
antithetic=False,
rank_transform=True):
r"""Initialize OpenAI-ES.
Args:
mu0 (ndarray): initial mean
std0 (float): initial standard deviation
popsize (int): population size
std_decay (float): standard deviation decay
min_std (float): minimum of standard deviation
lr (float): learning rate
lr_decay (float): learning rate decay
min_lr (float): minumum of learning rate
antithetic (bool): If True, then use antithetic sampling to generate population.
rank_transform (bool): If True, then use rank transformation of fitness (combat with outliers).
"""
self.mu0 = np.array(mu0)
self.std0 = std0
self.popsize = popsize
self.std_decay = std_decay
self.min_std = min_std
self.lr = lr
self.lr_decay = lr_decay
self.min_lr = min_lr
self.antithetic = antithetic
if self.antithetic:
assert self.popsize % 2 == 0, 'popsize must be even for antithetic sampling. '
self.rank_transform = rank_transform
if self.rank_transform:
self.rank_transformer = RankTransform()
# Some other settings
self.num_params = self.mu0.size
self.mu = torch.from_numpy(self.mu0).float()
self.mu.requires_grad = True # requires gradient for optimizer to update
self.std = self.std0
self.optimizer = optim.Adam([self.mu], lr=self.lr)
self.lr_scheduler = optim.lr_scheduler.ExponentialLR(
optimizer=self.optimizer, gamma=self.lr_decay)
self.solutions = None
self.best_param = None
self.best_f_val = None
self.hist_best_param = None
self.hist_best_f_val = None
def test_rank_transform(self):
rank_transform = RankTransform()
# scalar not allowed
with pytest.raises(AssertionError):
rank_transform(3)
# multidimensional array not allowed
with pytest.raises(AssertionError):
rank_transform([[1, 2, 3]])
# List
a = [3, 14, 1]
assert np.allclose(rank_transform(a, centered=False), [1, 2, 0])
assert np.allclose(rank_transform(a), [0, 0.5, -0.5])
# ndarray
b = np.array([3, 14, 1])
assert np.allclose(rank_transform(b, centered=False), [1, 2, 0])
assert np.allclose(rank_transform(b), [0, 0.5, -0.5])
def test_rank_transform(self):
rank_transform = RankTransform()
# List
a = [3, 14, 1]
assert np.allclose(rank_transform(a, centered=False), [1, 2, 0])
assert np.allclose(rank_transform(a), [0, 0.5, -0.5])
# ndarray
b = np.array([3, 14, 1])
assert np.allclose(rank_transform(b, centered=False), [1, 2, 0])
assert np.allclose(rank_transform(b), [0, 0.5, -0.5])
#
# Test exceptions
#
# Scalar is not allowed
with pytest.raises(AssertionError):
rank_transform(5)
# ndarray more than 1-dim is not allowed
c = np.array([[3, 14, 1]])
with pytest.raises(ValueError):
rank_transform(c)