def multivariate_state_std_gaussian():
print('Testing multivariate state std gaussian policy...')
n_dims = 5
n_outs = 3
std = np.random.randn(n_outs)
approximator_params = dict(input_dim=n_dims)
mu_approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(n_outs,),
params=approximator_params)
std_approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(n_outs,),
params=approximator_params)
pi = StateStdGaussianPolicy(mu_approximator, std_approximator)
mu_weights = np.random.rand(pi.weights_size)+0.1
pi.set_weights(mu_weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)
def univariate_gaussian():
print('Testing univariate gaussian policy...')
sigma = 1e-3*np.eye(1)
n_dims = 5
approximator_params = dict(input_dim=n_dims)
approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(1,),
params=approximator_params)
pi = GaussianPolicy(approximator, sigma)
mu_weights = np.random.rand(pi.weights_size)
pi.set_weights(mu_weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)
def test_multivariate_state_std_gaussian():
np.random.seed(88)
n_dims = 5
n_outs = 3
mu_approximator = Regressor(LinearApproximator,
input_shape=(n_dims, ),
output_shape=(n_outs, ))
std_approximator = Regressor(LinearApproximator,
input_shape=(n_dims, ),
output_shape=(n_outs, ))
pi = StateStdGaussianPolicy(mu_approximator, std_approximator)
weights = np.random.rand(pi.weights_size) + .1
pi.set_weights(weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)
def test_multivariate_gaussian():
np.random.seed(88)
n_dims = 5
n_outs = 3
random_matrix = np.random.rand(n_outs, n_outs)
sigma = random_matrix.dot(random_matrix.T)
approximator = Regressor(LinearApproximator,
input_shape=(n_dims, ),
output_shape=(n_outs, ))
pi = GaussianPolicy(approximator, sigma)
mu_weights = np.random.rand(pi.weights_size)
pi.set_weights(mu_weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)
def test_multivariate_state_std_gaussian():
np.random.seed(88)
n_dims = 5
n_outs = 3
mu_approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(n_outs,))
std_approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(n_outs,))
pi = StateStdGaussianPolicy(mu_approximator, std_approximator)
weights = np.random.rand(pi.weights_size) + .1
pi.set_weights(weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)
def test_multivariate_gaussian():
np.random.seed(88)
n_dims = 5
n_outs = 3
random_matrix = np.random.rand(n_outs, n_outs)
sigma = random_matrix.dot(random_matrix.T)
approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(n_outs,))
pi = GaussianPolicy(approximator, sigma)
mu_weights = np.random.rand(pi.weights_size)
pi.set_weights(mu_weights)
x = np.random.randn(20, n_dims)
for x_i in x:
state = np.atleast_1d(x_i)
action = pi.draw_action(state)
exact_diff = pi.diff(state, action)
numerical_diff = numerical_diff_policy(pi, state, action)
assert np.allclose(exact_diff, numerical_diff)