def point_test_grad_impl():
# check gradient implementation
rng = nprs(42)
test_ob = rng.uniform(size=(4, ))
test_act = rng.uniform(size=(4, ))
test_theta = rng.uniform(size=(4, 5))
# Check that the shape matches
assert point_get_grad_logp_action(test_theta, test_ob,
test_act).shape == test_theta.shape
gradient_check(
lambda x: point_get_logp_action(x.reshape(test_theta.shape), test_ob,
test_act),
lambda x: point_get_grad_logp_action(x.reshape(test_theta.shape),
test_ob, test_act).flatten(),
test_theta.flatten())
def point_test_grad_impl():
# check gradient implementation
rng = nprs(42)
test_ob = rng.uniform(size=(4,))
test_act = rng.uniform(size=(4,))
test_theta = rng.uniform(size=(4, 5))
# Check that the shape matches
assert point_get_grad_logp_action(
test_theta, test_ob, test_act).shape == test_theta.shape
gradient_check(
lambda x: point_get_logp_action(
x.reshape(test_theta.shape), test_ob, test_act),
lambda x: point_get_grad_logp_action(
x.reshape(test_theta.shape), test_ob, test_act).flatten(),
test_theta.flatten()
)