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greedy_reward_prediction_agent_test.py
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greedy_reward_prediction_agent_test.py
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# coding=utf-8
# Copyright 2020 The TF-Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for greedy_reward_prediction_agent.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
from absl.testing import parameterized
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from tf_agents.bandits.agents import greedy_reward_prediction_agent as greedy_agent
from tf_agents.bandits.agents import neural_boltzmann_agent
from tf_agents.bandits.agents import neural_epsilon_greedy_agent
from tf_agents.bandits.agents import neural_falcon_agent
from tf_agents.bandits.drivers import driver_utils
from tf_agents.bandits.networks import global_and_arm_feature_network
from tf_agents.bandits.policies import constraints
from tf_agents.bandits.specs import utils as bandit_spec_utils
from tf_agents.networks import network
from tf_agents.policies import utils as policy_utilities
from tf_agents.specs import tensor_spec
from tf_agents.trajectories import policy_step
from tf_agents.trajectories import time_step as ts
from tf_agents.utils import common
class DummyNet(network.Network):
def __init__(self, unused_observation_spec, action_spec, name=None):
super(DummyNet, self).__init__(
unused_observation_spec, state_spec=(), name=name
)
action_spec = tf.nest.flatten(action_spec)[0]
num_actions = action_spec.maximum - action_spec.minimum + 1
# Store custom layers that can be serialized through the Checkpointable API.
self._dummy_layers = [
tf.keras.layers.Dense(
num_actions,
kernel_initializer=tf.constant_initializer(
[[1, 1.5, 2], [1, 1.5, 4]]
),
bias_initializer=tf.constant_initializer([[1], [1], [-10]]),
)
]
def call(self, inputs, step_type=None, network_state=()):
del step_type
inputs = tf.cast(inputs, tf.float32)
for layer in self._dummy_layers:
inputs = layer(inputs)
return inputs, network_state
def _get_initial_and_final_steps(observations, rewards):
batch_size = tf.nest.flatten(observations)[0].shape[0]
if isinstance(observations, np.ndarray):
observations = tf.constant(
observations, dtype=tf.float32, name='observation'
)
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
tf.constant(0.0, dtype=tf.float32, shape=[batch_size], name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
tf.constant(rewards, dtype=tf.float32, name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
return initial_step, final_step
def _get_initial_and_final_steps_nested_rewards(observations, rewards):
batch_size = tf.nest.flatten(observations)[0].shape[0]
if isinstance(observations, np.ndarray):
observations = tf.constant(
observations, dtype=tf.float32, name='observation'
)
zero_rewards = {
'reward': tf.constant(0.0, dtype=tf.float32, shape=[batch_size]),
'constraint': tf.constant(0.0, dtype=tf.float32, shape=[batch_size]),
}
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
zero_rewards,
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
rewards_nest = tf.nest.map_structure(
lambda t: tf.convert_to_tensor(t, dtype=tf.float32), rewards
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
rewards_nest,
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
return initial_step, final_step
def _get_initial_and_final_steps_with_action_mask(observations, rewards):
batch_size = tf.nest.flatten(observations)[0].shape[0]
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
tf.constant(0.0, dtype=tf.float32, shape=[batch_size], name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(observations[0], observations[1]),
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
tf.constant(rewards, dtype=tf.float32, name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(
tf.nest.map_structure(lambda x: x + 100.0, observations[0]),
observations[1],
),
)
return initial_step, final_step
def _get_initial_and_final_steps_action_mask_nested_rewards(
observations, rewards
):
batch_size = tf.nest.flatten(observations)[0].shape[0]
zero_rewards = {
'reward': tf.constant(0.0, dtype=tf.float32, shape=[batch_size]),
'constraint': tf.constant(0.0, dtype=tf.float32, shape=[batch_size]),
}
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
zero_rewards,
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(observations[0], observations[1]),
)
rewards_nest = tf.nest.map_structure(
lambda t: tf.convert_to_tensor(t, dtype=tf.float32), rewards
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
rewards_nest,
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(
tf.nest.map_structure(lambda x: x + 100.0, observations[0]),
observations[1],
),
)
return initial_step, final_step
def _get_action_step(action):
return policy_step.PolicyStep(
action=tf.convert_to_tensor(action), info=policy_utilities.PolicyInfo()
)
def _get_experience(initial_step, action_step, final_step):
single_experience = driver_utils.trajectory_for_bandit(
initial_step, action_step, final_step
)
# Adds a 'time' dimension.
return tf.nest.map_structure(
lambda x: tf.expand_dims(tf.convert_to_tensor(x), 1), single_experience
)
def test_cases():
return parameterized.named_parameters(
{
'testcase_name': 'Greedy',
'agent_class': greedy_agent.GreedyRewardPredictionAgent,
'use_num_samples_list': True,
},
{
'testcase_name': 'Falcon',
'agent_class': neural_falcon_agent.NeuralFalconAgent,
'use_num_samples_list': True,
},
{
'testcase_name': 'Boltzmann',
'agent_class': neural_boltzmann_agent.NeuralBoltzmannAgent,
'use_num_samples_list': True,
},
{
'testcase_name': 'EpsilonGreedy',
'agent_class': functools.partial(
neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent, epsilon=0.05
),
'use_num_samples_list': False,
},
)
class AgentTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
super(AgentTest, self).setUp()
tf.compat.v1.enable_resource_variables()
self._obs_spec = tensor_spec.TensorSpec([2], tf.float32)
self._time_step_spec = ts.time_step_spec(self._obs_spec)
self._action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=2
)
self._observation_spec = self._time_step_spec.observation
self._num_samples_list = [
tf.compat.v2.Variable(0, dtype=tf.int64, name='num_samples_0'),
tf.compat.v2.Variable(0, dtype=tf.int64, name='num_samples_1'),
tf.compat.v2.Variable(0, dtype=tf.int64, name='num_samples_2'),
]
self._per_arm_num_samples_list = [
tf.compat.v2.Variable(0, dtype=tf.int64, name='num_samples')
]
@test_cases()
def testCreateAgent(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=None,
)
self.assertIsNotNone(agent.policy)
@test_cases()
def testInitializeAgent(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=None,
)
init_op = agent.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
@test_cases()
def testLoss(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps_nested_rewards(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=None,
)
init_op = agent.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
loss, _ = agent._loss(experience)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.assertAllClose(self.evaluate(loss), 42.25)
def testPolicy(self):
reward_net = DummyNet(self._observation_spec, self._action_spec)
agent = greedy_agent.GreedyRewardPredictionAgent(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=None,
num_samples_list=self._num_samples_list,
)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_steps = ts.restart(observations, batch_size=2)
policy = agent.policy
action_step = policy.action(time_steps)
# Batch size 2.
self.assertAllEqual([2], action_step.action.shape)
self.evaluate(tf.compat.v1.initialize_all_variables())
actions = self.evaluate(action_step.action)
self.assertAllEqual(actions, [1, 2])
def testInitializeRestoreAgent(self):
reward_net = DummyNet(self._observation_spec, self._action_spec)
agent = greedy_agent.GreedyRewardPredictionAgent(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=None,
num_samples_list=self._num_samples_list,
)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_steps = ts.restart(observations, batch_size=2)
policy = agent.policy
action_step = policy.action(time_steps)
self.evaluate(tf.compat.v1.initialize_all_variables())
checkpoint = tf.train.Checkpoint(agent=agent)
latest_checkpoint = tf.train.latest_checkpoint(self.get_temp_dir())
checkpoint_load_status = checkpoint.restore(latest_checkpoint)
if tf.executing_eagerly():
self.evaluate(checkpoint_load_status.initialize_or_restore())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
else:
with self.cached_session() as sess:
checkpoint_load_status.initialize_or_restore(sess)
self.assertAllEqual(sess.run(action_step.action), [1, 2])
@test_cases()
def testTrainAgent(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
)
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss_before, _ = agent.train(experience, None)
loss_after, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.assertAllClose(self.evaluate(loss_before), 42.25)
self.assertAllClose(self.evaluate(loss_after), 93.46)
@test_cases()
def testTrainAgentWithConstraint(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
constraint_net = DummyNet(self._observation_spec, self._action_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net,
)
reward_spec = {
'reward': tensor_spec.TensorSpec(
shape=(), dtype=tf.float32, name='reward'
),
'constraint': tensor_spec.TensorSpec(
shape=(1,), dtype=tf.float32, name='constraint'
),
}
self._time_step_spec = ts.time_step_spec(self._obs_spec, reward_spec)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
constraints=[neural_constraint],
)
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = {
'reward': np.array([0.5, 3.0], dtype=np.float32),
'constraint': np.array([[6.0], [4.0]], dtype=np.float32),
}
initial_step, final_step = _get_initial_and_final_steps_nested_rewards(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss_before, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
# The loss is the sum of the reward loss and the constraint loss.
self.assertAllClose(self.evaluate(loss_before), 42.25 + 30.125)
@test_cases()
def testTrainAgentWithMask(self, agent_class, use_num_samples_list):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
time_step_spec = ts.time_step_spec((
tensor_spec.TensorSpec([2], tf.float32),
tensor_spec.TensorSpec([3], tf.int32),
))
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
observation_and_action_constraint_splitter=lambda x: (x[0], x[1]),
)
observations = (
np.array([[1, 2], [3, 4]], dtype=np.float32),
np.array([[1, 0, 0], [1, 1, 0]], dtype=np.int32),
)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps_with_action_mask(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss_before, _ = agent.train(experience, None)
loss_after, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.assertAllClose(self.evaluate(loss_before), 42.25)
self.assertAllClose(self.evaluate(loss_after), 93.46)
@test_cases()
def testTrainAgentWithMaskAndConstraint(
self, agent_class, use_num_samples_list
):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
reward_spec = {
'reward': tensor_spec.TensorSpec(
shape=(), dtype=tf.float32, name='reward'
),
'constraint': tensor_spec.TensorSpec(
shape=(1,), dtype=tf.float32, name='constraint'
),
}
observation_and_mask_spec = (
tensor_spec.TensorSpec([2], tf.float32),
tensor_spec.TensorSpec([3], tf.int32),
)
time_step_spec = ts.time_step_spec(observation_and_mask_spec, reward_spec)
constraint_net = DummyNet(self._observation_spec, self._action_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net,
)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
observation_and_action_constraint_splitter=lambda x: (x[0], x[1]),
constraints=[neural_constraint],
)
observations = (
np.array([[1, 2], [3, 4]], dtype=np.float32),
np.array([[1, 0, 0], [1, 1, 0]], dtype=np.int32),
)
actions = np.array([0, 1], dtype=np.int32)
rewards = {
'reward': np.array([0.5, 3.0], dtype=np.float32),
'constraint': np.array([[6.0], [4.0]], dtype=np.float32),
}
initial_step, final_step = (
_get_initial_and_final_steps_action_mask_nested_rewards(
observations, rewards
)
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss_before, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
# The loss is the sum of the reward loss and the constraint loss.
self.assertAllClose(self.evaluate(loss_before), 42.25 + 30.125)
@test_cases()
def testTrainAgentWithLaplacianSmoothing(
self, agent_class, use_num_samples_list
):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
laplacian_matrix = tf.constant(
[[1.0, -1.0, 0.0], [-1.0, 2.0, -1.0], [0.0, -1.0, 1.0]]
)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
laplacian_matrix=laplacian_matrix,
laplacian_smoothing_weight=1.0,
)
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss_before, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
# The Laplacian smoothing term ends up adding 22.5 to the loss.
self.assertAllClose(self.evaluate(loss_before), 42.25 + 22.5)
@test_cases()
def testTrainAgentWithLaplacianSmoothingInvalidMatrix(
self, agent_class, use_num_samples_list
):
if tf.executing_eagerly:
return
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError, ''):
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
# Set the Laplacian matrix to be the identity, which is not a valid
# Laplacian.
laplacian_matrix = tf.eye(3)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._num_samples_list
)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
laplacian_matrix=laplacian_matrix,
laplacian_smoothing_weight=1.0,
)
self.evaluate(tf.compat.v1.initialize_all_variables())
loss_before, _ = agent.train(experience, None)
self.evaluate(loss_before)
@test_cases()
def testTrainPerArmAgent(self, agent_class, use_num_samples_list):
obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
2, 3, 4, add_num_actions_feature=True
)
time_step_spec = ts.time_step_spec(obs_spec)
reward_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
obs_spec, (4, 3), (3, 4), (4, 2)
)
)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._per_arm_num_samples_list
)
agent = agent_class(
time_step_spec,
self._action_spec,
reward_network=reward_net,
accepts_per_arm_features=True,
optimizer=optimizer,
)
observations = {
bandit_spec_utils.GLOBAL_FEATURE_KEY: tf.constant(
[[1, 2], [3, 4]], dtype=tf.float32
),
bandit_spec_utils.PER_ARM_FEATURE_KEY: tf.cast(
tf.reshape(tf.range(24), shape=[2, 4, 3]), dtype=tf.float32
),
bandit_spec_utils.NUM_ACTIONS_FEATURE_KEY: tf.ones([2], dtype=tf.int32),
}
actions = np.array([0, 3], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = policy_step.PolicyStep(
action=tf.convert_to_tensor(actions),
info=policy_utilities.PerArmPolicyInfo(
chosen_arm_features=np.array(
[[1, 2, 3], [3, 2, 1]], dtype=np.float32
)
),
)
experience = _get_experience(initial_step, action_step, final_step)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.evaluate(agent.train(experience, None).loss)
if use_num_samples_list:
self.evaluate(agent.train(experience, None).loss)
self.assertEqual(
self.evaluate(self._per_arm_num_samples_list[0].read_value()), 4
)
@test_cases()
def testNumSamplesList(self, agent_class, use_num_samples_list):
if not use_num_samples_list:
return
reward_net = DummyNet(self._observation_spec, self._action_spec)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
agent = agent_class(
self._time_step_spec,
self._action_spec,
reward_network=reward_net,
optimizer=optimizer,
num_samples_list=self._num_samples_list,
)
observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.5, 3.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.evaluate(agent.train(experience, None).loss)
self.evaluate(agent.train(experience, None).loss)
# Action 0 and 1 have 2 samples. Action 2 has 0 samples.
self.assertEqual(self.evaluate(self._num_samples_list[0].read_value()), 2)
self.assertEqual(self.evaluate(self._num_samples_list[1].read_value()), 2)
self.assertEqual(self.evaluate(self._num_samples_list[2].read_value()), 0)
@test_cases()
def testTrainPerArmAgentWithConstraint(
self, agent_class, use_num_samples_list
):
obs_spec = bandit_spec_utils.create_per_arm_observation_spec(2, 3, 4)
reward_spec = {
'reward': tensor_spec.TensorSpec(
shape=(), dtype=tf.float32, name='reward'
),
'constraint': tensor_spec.TensorSpec(
shape=(1,), dtype=tf.float32, name='constraint'
),
}
time_step_spec = ts.time_step_spec(obs_spec, reward_spec)
reward_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
obs_spec, (4, 3), (3, 4), (4, 2)
)
)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.1)
constraint_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
obs_spec, (4, 3), (3, 4), (4, 2)
)
)
neural_constraint = constraints.NeuralConstraint(
time_step_spec, self._action_spec, constraint_network=constraint_net
)
if use_num_samples_list:
agent_class = functools.partial(
agent_class, num_samples_list=self._per_arm_num_samples_list
)
agent = agent_class(
time_step_spec,
self._action_spec,
reward_network=reward_net,
accepts_per_arm_features=True,
optimizer=optimizer,
constraints=[neural_constraint],
)
observations = {
bandit_spec_utils.GLOBAL_FEATURE_KEY: tf.constant(
[[1, 2], [3, 4]], dtype=tf.float32
),
bandit_spec_utils.PER_ARM_FEATURE_KEY: tf.cast(
tf.reshape(tf.range(24), shape=[2, 4, 3]), dtype=tf.float32
),
}
actions = np.array([0, 3], dtype=np.int32)
rewards = {
'reward': np.array([0.5, 3.0], dtype=np.float32),
'constraint': np.array([[6.0], [4.0]], dtype=np.float32),
}
initial_step, final_step = _get_initial_and_final_steps_nested_rewards(
observations, rewards
)
action_step = policy_step.PolicyStep(
action=tf.convert_to_tensor(actions),
info=policy_utilities.PerArmPolicyInfo(
chosen_arm_features=np.array(
[[1, 2, 3], [3, 2, 1]], dtype=np.float32
)
),
)
experience = _get_experience(initial_step, action_step, final_step)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.evaluate(agent.train(experience, None).loss)
if use_num_samples_list:
self.evaluate(agent.train(experience, None).loss)
self.assertEqual(
self.evaluate(self._per_arm_num_samples_list[0].read_value()), 4
)
if __name__ == '__main__':
tf.test.main()