def testPolicyWithConstraints(self):
constraint_net = DummyNet(self._obs_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
tf.compat.v1.set_random_seed(1)
policy = greedy_reward_policy.GreedyRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec),
constraints=[neural_constraint],
emit_policy_info=('predicted_rewards_mean',))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllEqual(self.evaluate(action_step.action), [1, 2])
# The expected values are obtained by passing the observation through the
# Keras dense layer of the DummyNet (defined above).
predicted_rewards_expected_array = np.array([[4.0, 5.5, 0.0],
[8.0, 11.5, 12.0]])
p_info = self.evaluate(action_step.info)
self.assertAllClose(p_info.predicted_rewards_mean,
predicted_rewards_expected_array)
def testComputeLossWithArmFeatures(self):
obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
global_dim=2, per_arm_dim=3, max_num_actions=3)
time_step_spec = ts.time_step_spec(obs_spec)
constraint_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
obs_spec,
global_layers=(4,),
arm_layers=(4,),
common_layers=(4,)))
neural_constraint = constraints.NeuralConstraint(
time_step_spec,
self._action_spec,
constraint_network=constraint_net)
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(18), shape=[2, 3, 3]), dtype=tf.float32)
}
actions = tf.constant([0, 1], dtype=tf.int32)
rewards = tf.constant([0.5, 3.0], dtype=tf.float32)
init_op = neural_constraint.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
loss = neural_constraint.compute_loss(
observations,
actions,
rewards)
self.assertGreater(self.evaluate(loss), 0.0)
def testInitializeConstraint(self):
constraint_net = DummyNet(self._observation_spec, self._action_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
init_op = neural_constraint.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
def testTrainPerArmAgentWithConstraint(self):
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=(),
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)
agent = greedy_agent.GreedyRewardPredictionAgent(
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)
agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
def testComputeActionFeasibility(self):
constraint_net = DummyNet(self._observation_spec, self._action_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
init_op = neural_constraint.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
observation = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
feasibility_prob = neural_constraint(observation)
self.assertAllClose(self.evaluate(feasibility_prob), np.ones([2, 3]))
def testComputeFeasibilityMaskWithActionMask(self):
observation_spec = tensor_spec.TensorSpec([2], tf.float32)
time_step_spec = ts.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)
constraint_net = DummyNet(observation_spec, action_spec)
neural_constraint = constraints.NeuralConstraint(
time_step_spec,
action_spec,
constraint_network=constraint_net)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
action_mask = tf.constant([[0, 0, 1], [0, 1, 0]], dtype=tf.int32)
feasibility_prob = constraints.compute_feasibility_probability(
observations, [neural_constraint], batch_size=2, num_actions=3,
action_mask=action_mask)
self.assertAllEqual(self.evaluate(tf.cast(action_mask, tf.float32)),
self.evaluate(feasibility_prob))
def testComputeLoss(self):
constraint_net = DummyNet(self._observation_spec, self._action_spec)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
actions = tf.constant([0, 1], dtype=tf.int32)
rewards = tf.constant([0.5, 3.0], dtype=tf.float32)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
init_op = neural_constraint.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
loss = neural_constraint.compute_loss(observations, actions, rewards)
self.assertAllClose(self.evaluate(loss), 42.25)
def testTrainAgentWithMaskAndConstraint(self):
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=(),
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)
agent = greedy_agent.GreedyRewardPredictionAgent(
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)
def testComputeMaskFromMultipleSourcesMask(self):
observation_spec = bandit_spec_utils.create_per_arm_observation_spec(
4, 5, 6)
time_step_spec = ts.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 5)
constraint_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
observation_spec, (3, 4), (4, 3), (2, 3)))
neural_constraint = constraints.NeuralConstraint(
time_step_spec,
action_spec,
constraint_network=constraint_net)
original_mask = [[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]]
observations = ({
'global': tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32),
'per_arm': tf.reshape(tf.range(60, dtype=tf.float32), shape=[2, 6, 5]),
}, original_mask)
mask = constraints.construct_mask_from_multiple_sources(
observations, lambda x: (x[0], x[1]), [neural_constraint], 6)
self.assertAllGreaterEqual(original_mask - mask, 0)
def testComputeMaskFromMultipleSourcesNumActionsFeature(self):
observation_spec = bandit_spec_utils.create_per_arm_observation_spec(
4, 5, 6, add_num_actions_feature=True)
time_step_spec = ts.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 5)
constraint_net = (
global_and_arm_feature_network.create_feed_forward_common_tower_network(
observation_spec, (3, 4), (4, 3), (2, 3)))
neural_constraint = constraints.NeuralConstraint(
time_step_spec,
action_spec,
constraint_network=constraint_net)
observations = {
'global': tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=tf.float32),
'per_arm': tf.reshape(tf.range(60, dtype=tf.float32), shape=[2, 6, 5]),
'num_actions': tf.constant([4, 3], dtype=tf.int32)
}
mask = constraints.construct_mask_from_multiple_sources(
observations, None, [neural_constraint], 6)
implied_mask = [[1, 1, 1, 1, 0, 0], [1, 1, 1, 0, 0, 0]]
self.assertAllGreaterEqual(implied_mask - mask, 0)
def testPolicyWithConstraints(self):
constraint_net = DummyNet(self._obs_spec)
neural_constraint = constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
tf.compat.v1.set_random_seed(1)
policy = boltzmann_reward_policy.BoltzmannRewardPredictionPolicy(
self._time_step_spec,
self._action_spec,
reward_network=DummyNet(self._obs_spec),
constraints=[neural_constraint],
emit_policy_info=('predicted_rewards_mean', ))
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations, batch_size=2)
action_step = policy.action(time_step, seed=1)
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
# Initialize all variables
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllInSet(self.evaluate(action_step.action), [1, 2])
def testCreateConstraint(self):
constraint_net = DummyNet(self._observation_spec, self._action_spec)
constraints.NeuralConstraint(
self._time_step_spec,
self._action_spec,
constraint_network=constraint_net)
