def testUCBandThompsonSamplingShareVariables(self):
if not tf.executing_eagerly():
self.skipTest('Test only works in eager mode.')
context_dim = 9
num_actions = 4
batch_size = 7
variable_collection = linear_agent.LinearBanditVariableCollection(
context_dim=context_dim, num_models=num_actions)
observation_spec = tensor_spec.TensorSpec([context_dim], tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec(dtype=tf.int32,
shape=(),
minimum=0,
maximum=num_actions - 1)
ucb_agent = lin_ucb_agent.LinearUCBAgent(
time_step_spec=time_step_spec,
action_spec=action_spec,
variable_collection=variable_collection)
ts_agent = linear_thompson_sampling_agent.LinearThompsonSamplingAgent(
time_step_spec=time_step_spec,
action_spec=action_spec,
variable_collection=variable_collection)
initial_step, final_step = _get_initial_and_final_steps(
batch_size, context_dim)
action = np.random.randint(num_actions,
size=batch_size,
dtype=np.int32)
action_step = _get_action_step(action)
experience = _get_experience(initial_step, action_step, final_step)
self.evaluate(ucb_agent.train(experience))
self.assertAllEqual(ucb_agent._variable_collection.cov_matrix_list[0],
ts_agent._variable_collection.cov_matrix_list[0])
self.evaluate(ts_agent.train(experience))
self.assertAllEqual(ucb_agent._variable_collection.data_vector_list[0],
ts_agent._variable_collection.data_vector_list[0])
def testLinearAgentFinalTheta(self, set_example_weights):
num_actions = 1
context_dim = 1
batch_size = 10
use_eigendecomp = False
dtype = tf.float32
# The observation consists of a single constant feature.
initial_step, final_step = _get_initial_and_final_steps(
batch_size, context_dim, use_constant_observations=True)
action = [0] * batch_size
action_step = _get_action_step(action)
experience = _get_experience(initial_step, action_step, final_step)
# Construct an agent and perform the update.
observation_spec = tensor_spec.TensorSpec([context_dim], tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1)
variable_collection = linear_agent.LinearBanditVariableCollection(
context_dim, num_actions, use_eigendecomp=use_eigendecomp, dtype=dtype)
agent = linear_agent.LinearBanditAgent(
exploration_policy=linear_agent.ExplorationPolicy.linear_ucb_policy,
time_step_spec=time_step_spec,
action_spec=action_spec,
variable_collection=variable_collection,
use_eigendecomp=use_eigendecomp,
tikhonov_weight=0.0,
dtype=dtype)
self.evaluate(agent.initialize())
reward = tf.reshape(experience.reward, [batch_size])
weights = tf.linspace(
start=1.5, stop=10.5, num=batch_size) if set_example_weights else None
self.evaluate(agent.train(experience, weights=weights))
final_theta = self.evaluate(agent.theta)
self.assertAllClose(tf.shape(final_theta), [1, 1])
# Because the observation consists of a single constant feature and the
# agent uses zero regularization for training (`tikhonov_weight` set to 0),
# the final theta is expected to be the average reward when the weights are
# unset, and the weighted average reward when the weights are set.
if weights is None:
self.assertAllClose(final_theta[0, 0], tf.reduce_mean(reward))
else:
self.assertAllClose(
final_theta[0, 0],
tf.reduce_sum(weights * reward) / tf.reduce_sum(weights))
def testInitializeRestoreVariableCollection(self):
if not tf.executing_eagerly():
self.skipTest('Test only works in eager mode.')
context_dim = 7
num_actions = 5
variable_collection = linear_agent.LinearBanditVariableCollection(
context_dim=context_dim, num_models=num_actions)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(variable_collection.num_samples_list)
checkpoint = tf.train.Checkpoint(variable_collection=variable_collection)
checkpoint_dir = self.get_temp_dir()
checkpoint_prefix = os.path.join(checkpoint_dir, 'checkpoint')
checkpoint.save(file_prefix=checkpoint_prefix)
variable_collection.num_samples_list[2].assign(14)
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
checkpoint_load_status = checkpoint.restore(latest_checkpoint)
self.evaluate(checkpoint_load_status.initialize_or_restore())
self.assertEqual(self.evaluate(variable_collection.num_samples_list[2]), 0)
def testLinearAgentUpdateWithBias(self,
batch_size,
context_dim,
exploration_policy,
dtype,
use_eigendecomp=False):
"""Check that the agent updates for specified actions and rewards."""
# Construct a `Trajectory` for the given action, observation, reward.
num_actions = 5
initial_step, final_step = _get_initial_and_final_steps(
batch_size, context_dim)
action = np.random.randint(num_actions,
size=batch_size,
dtype=np.int32)
action_step = _get_action_step(action)
experience = _get_experience(initial_step, action_step, final_step)
# Construct an agent and perform the update.
observation_spec = tensor_spec.TensorSpec([context_dim], tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec(dtype=tf.int32,
shape=(),
minimum=0,
maximum=num_actions - 1)
variable_collection = linear_agent.LinearBanditVariableCollection(
context_dim + 1, num_actions, use_eigendecomp, dtype)
agent = linear_agent.LinearBanditAgent(
exploration_policy=exploration_policy,
time_step_spec=time_step_spec,
action_spec=action_spec,
variable_collection=variable_collection,
use_eigendecomp=use_eigendecomp,
add_bias=True,
dtype=dtype)
self.evaluate(agent.initialize())
loss_info = agent.train(experience)
self.evaluate(loss_info)
final_a = self.evaluate(agent.cov_matrix)
final_b = self.evaluate(agent.data_vector)
final_theta = self.evaluate(agent.theta)
# Compute the expected updated estimates.
observations_list = tf.dynamic_partition(
data=tf.reshape(experience.observation, [batch_size, context_dim]),
partitions=tf.convert_to_tensor(action),
num_partitions=num_actions)
rewards_list = tf.dynamic_partition(
data=tf.reshape(experience.reward, [batch_size]),
partitions=tf.convert_to_tensor(action),
num_partitions=num_actions)
expected_a_updated_list = []
expected_b_updated_list = []
expected_theta_updated_list = []
for _, (observations_for_arm, rewards_for_arm) in enumerate(
zip(observations_list, rewards_list)):
observations_for_arm = tf.concat([
observations_for_arm,
tf.ones_like(observations_for_arm[:, 0:1])
],
axis=1)
num_samples_for_arm_current = tf.cast(
tf.shape(rewards_for_arm)[0], tf.float32)
num_samples_for_arm_total = num_samples_for_arm_current
# pylint: disable=cell-var-from-loop
def true_fn():
a_new = tf.matmul(observations_for_arm,
observations_for_arm,
transpose_a=True)
b_new = bandit_utils.sum_reward_weighted_observations(
rewards_for_arm, observations_for_arm)
return a_new, b_new
def false_fn():
return tf.zeros([context_dim + 1,
context_dim + 1]), tf.zeros([context_dim + 1])
a_new, b_new = tf.cond(
tf.squeeze(num_samples_for_arm_total) > 0, true_fn, false_fn)
theta_new = tf.squeeze(tf.linalg.solve(
a_new + tf.eye(context_dim + 1), tf.expand_dims(b_new,
axis=-1)),
axis=-1)
expected_a_updated_list.append(self.evaluate(a_new))
expected_b_updated_list.append(self.evaluate(b_new))
expected_theta_updated_list.append(self.evaluate(theta_new))
# Check that the actual updated estimates match the expectations.
self.assertAllClose(expected_a_updated_list, final_a)
self.assertAllClose(expected_b_updated_list, final_b)
self.assertAllClose(self.evaluate(
tf.stack(expected_theta_updated_list)),
final_theta,
atol=0.1,
rtol=0.05)
