def test_likelihood(self):
"""Check likelihood and log-likelihood computation."""
net_in = tf.placeholder(tf.float64, shape=(None, 2), name='input')
log_std = tf.placeholder(tf.float64, shape=(), name='log_std')
net_out = net_in
policy = policies.GaussianPolicy(net_in, net_out, 2, log_std)
with self.session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for _ in range(100):
test_in = np.random.randn(1, 2)
log_std_in = np.random.randn()
test_actions = np.random.randn(1, 2)
lik = sess.run(policy.likelihood_op(test_actions), {
net_in: test_in,
log_std: log_std_in
})
log_lik = sess.run(policy.log_likelihood_op(test_actions), {
net_in: test_in,
log_std: log_std_in
})
# Likelihood
lik_test = np.zeros(2)
lik_test[0] = scipy.stats.multivariate_normal.pdf(
test_actions[0, 0],
mean=test_in.ravel()[0],
cov=np.exp(log_std_in)**2)
lik_test[1] = scipy.stats.multivariate_normal.pdf(
test_actions[0, 1],
mean=test_in.ravel()[1],
cov=np.exp(log_std_in)**2)
self.assertAllClose(lik_test[0] * lik_test[1], lik[0, 0])
# Log likelihood
log_lik_test = np.zeros(2)
log_lik_test[0] = scipy.stats.multivariate_normal.logpdf(
test_actions[0, 0],
mean=test_in.ravel()[0],
cov=np.exp(log_std_in)**2)
log_lik_test[1] = scipy.stats.multivariate_normal.logpdf(
test_actions[0, 1],
mean=test_in.ravel()[1],
cov=np.exp(log_std_in)**2)
self.assertAllClose(log_lik_test[0] + log_lik_test[1],
log_lik[0, 0])
def test_sample(self):
"""Verify whether samples of policy are approximately Gaussian."""
net_in = tf.placeholder(tf.float64, shape=(None, 1), name='input')
log_std = tf.placeholder(tf.float64, shape=(), name='log_std')
net_out = net_in
policy = policies.GaussianPolicy(net_in, net_out, 1, log_std)
with self.session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
test_in = np.array([[
np.random.randn(),
]])
test_log_std = np.random.uniform(-1, 0.5)
test_in_mult = np.repeat(test_in, 10000, axis=0)
# Test various ways to create samples
samples_1 = policy.sample(test_in_mult, sess,
{log_std: test_log_std})
samples_2 = sess.run(policy.sample_op()[0], {
net_in: test_in_mult,
log_std: test_log_std
})
samples_3 = sess.run(
policy.sample_op(10000)[0], {
net_in: test_in,
log_std: test_log_std
}).ravel()
# Check if approximately normally distributed
samples = [samples_1, samples_2, samples_3]
pvals = [
scipy.stats.normaltest((s - test_in[0, 0]) / test_log_std)[1]
for s in samples
]
means = [s.mean() for s in samples]
for pval in pvals:
self.assertGreater(pval, 1e-4)
# Verify shapes
self.assertEqual(samples[0].shape[0], test_in_mult.shape[0])
self.assertEqual(samples[0].shape[1], test_in_mult.shape[1])
self.assertEqual(samples[1].shape[0], test_in_mult.shape[0])
self.assertEqual(samples[1].shape[1], test_in_mult.shape[1])
# Check if sample mean corresponds to actual mean
for mean in means:
self.assertAlmostEqual(mean, test_in[0, 0], places=1)
def test_mean_std(self):
"""Simple check of the mean and standard deviation."""
net_in = tf.placeholder(tf.float64, shape=(None, 1), name='input')
log_std = tf.placeholder(tf.float64, shape=(), name='log_std')
net_out = net_in
policy = policies.GaussianPolicy(net_in, net_out, 1, log_std)
with self.session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for _ in range(10):
test_in = np.random.randn(1, 1)
log_std_in = np.random.randn()
mean, std, log_std_out = sess.run(policy.mean_std_log_std_op(), {
net_in: test_in,
log_std: log_std_in
})
self.assertAlmostEqual(mean[0, 0], test_in[0, 0])
self.assertAlmostEqual(log_std_out, log_std_in)
self.assertAlmostEqual(std, np.exp(log_std_in))
def test_learn_simple_policy(self):
"""Train a gaussian "policy" to react differently to various inputs.
Inputs are sampled from a 2D Gaussian distribution.
Outputs are one dimensional.
"""
input_means = np.array([[-1., -1], [-1, 1], [1, -1], [1, 1]])
input_std = .1
output_means = np.array([[0.], [1], [2], [3]])
network_generator = networks.FullyConnectedNetworkGenerator(
2, 1, (
64,
64,
), tf.nn.relu)
weights = network_generator.construct_network_weights()
net_in = tf.placeholder(tf.float32, shape=(None, 2), name='input')
net_out = network_generator.construct_network(net_in, weights)
policy = policies.GaussianPolicy(net_in, net_out, 1, -5.)
actions = tf.placeholder(tf.float32, shape=(None, 1), name='actions')
log_lik = policy.log_likelihood_op(actions)
optimizer = tf.train.AdamOptimizer(0.001)
minimizer = optimizer.minimize(-tf.reduce_mean(log_lik))
pol_mean, _ = policy.mean_op()
with self.session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for _ in range(1000):
sample_input = np.repeat(input_means, 100, axis=0)
sample_input += np.random.normal(0, input_std,
sample_input.shape)
sample_output = np.repeat(output_means, 100, axis=0)
sess.run(minimizer, {
net_in: sample_input,
actions: sample_output
})
output_means_res = sess.run(pol_mean, {net_in: input_means})
mae = np.mean(np.abs(output_means - output_means_res))
self.assertAlmostEqual(mae, 0, places=1)
