def test_model_policy_gradient_with_random_seed():
x0 = np.random.randn(5)
result1 = model_policy_gradient(
sum_of_squares,
x0,
learning_rate=1e-1,
decay_rate=0.96,
decay_steps=10,
log_sigma_init=-6.0,
max_iterations=50,
batch_size=30,
radius_coeff=3.0,
warmup_steps=10,
random_state=65536,
)
result2 = model_policy_gradient(
sum_of_squares,
x0,
learning_rate=1e-1,
decay_rate=0.96,
decay_steps=10,
log_sigma_init=-6.0,
max_iterations=50,
batch_size=30,
radius_coeff=3.0,
warmup_steps=10,
random_state=65536,
)
np.testing.assert_equal(result1, result2)
def test_model_policy_gradient_limited_iterations():
x0 = np.random.randn(10)
result = model_policy_gradient(
sum_of_squares,
x0,
learning_rate=1e-1,
decay_rate=0.96,
decay_steps=10,
log_sigma_init=-6.0,
batch_size=30,
radius_coeff=3.0,
warmup_steps=10,
known_values=None,
max_iterations=15,
)
assert isinstance(result.x, np.ndarray)
assert isinstance(result.fun, float)
assert result.nit == 15
def test_model_policy_gradient():
x0 = np.random.randn(5)
result = model_policy_gradient(
sum_of_squares,
x0,
learning_rate=1e-1,
decay_rate=0.96,
decay_steps=10,
log_sigma_init=-6.0,
max_iterations=100,
batch_size=30,
radius_coeff=3.0,
warmup_steps=10,
known_values=None,
)
np.testing.assert_allclose(result.x, np.zeros(len(result.x)), atol=1e-2)
np.testing.assert_allclose(result.fun, 0, atol=1e-7)
assert isinstance(result.nfev, int)
def test_model_policy_gradient_with_known_values():
x0 = np.random.randn(5)
known_xs = [np.ones(5)]
known_ys = [10.0]
_ = model_policy_gradient(
sum_of_squares,
x0,
learning_rate=1e-1,
decay_rate=0.96,
decay_steps=10,
log_sigma_init=-6.0,
max_iterations=50,
batch_size=30,
radius_coeff=3.0,
warmup_steps=10,
known_values=(known_xs, known_ys),
)
assert len(known_xs) == 1
assert len(known_ys) == 1