code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def call(self, trajectory):
"""Update the regret value.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
"""
baseline_reward = self._baseline_reward_fn(trajectory.observation)
trajectory_reward = trajectory.reward
if isinstance(traj... | Update the regret value.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def __init__(
self,
baseline_action_fn: Callable[[types.Tensor], types.Tensor],
name: Optional[Text] = 'SuboptimalArmsMetric',
dtype: float = tf.float32,
):
"""Computes the number of suboptimal arms with respect to a baseline.
Args:
baseline_action_fn: function that computes the... | Computes the number of suboptimal arms with respect to a baseline.
Args:
baseline_action_fn: function that computes the action used as a baseline
for computing the metric.
name: (str) name of the metric
dtype: dtype of the metric value.
| __init__ | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def call(self, trajectory):
"""Update the metric value.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
"""
baseline_action = self._baseline_action_fn(trajectory.observation)
disagreement = tf.cast(
tf.not_equal(baseline_action... | Update the metric value.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def __init__(
self,
constraint: constraints.BaseConstraint,
name: Optional[Text] = 'ConstraintViolationMetric',
dtype: float = tf.float32,
):
"""Computes the constraint violations given an input constraint.
Given a certain constraint, this metric computes how often the selected
ac... | Computes the constraint violations given an input constraint.
Given a certain constraint, this metric computes how often the selected
actions in the trajectory violate the constraint.
Args:
constraint: an instance of `tf_agents.bandits.policies.BaseConstraint`.
name: (str) name of the metric
... | __init__ | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def call(self, trajectory):
"""Update the constraint violations metric.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
"""
feasibility_prob_all_actions = self._constraint(trajectory.observation)
feasibility_prob_selected_actions = com... | Update the constraint violations metric.
Args:
trajectory: A tf_agents.trajectory.Trajectory
Returns:
The arguments, for easy chaining.
| call | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def __init__(
self,
estimated_reward_fn: Callable[[types.Tensor], types.Tensor],
name: Optional[Text] = 'DistanceFromGreedyMetric',
dtype: float = tf.float32,
):
"""Init function for the metric.
Args:
estimated_reward_fn: A function that takes the observation as input and
... | Init function for the metric.
Args:
estimated_reward_fn: A function that takes the observation as input and
computes the estimated rewards that the greedy policy uses.
name: (str) name of the metric
dtype: dtype of the metric value.
| __init__ | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics.py | Apache-2.0 |
def __call__(self, observation, actions=None):
"""Returns the probability of input actions being feasible."""
if actions is None:
actions = tf.range(
self._action_spec.minimum, self._action_spec.maximum + 1
)
actions = tf.reshape(actions, [1, -1])
actions = tf.tile(actions, [se... | Returns the probability of input actions being feasible. | __call__ | python | tensorflow/agents | tf_agents/bandits/metrics/tf_metrics_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/metrics/tf_metrics_test.py | Apache-2.0 |
def _validate_scalarization_parameter_shape(
multi_objectives: tf.Tensor,
params: Dict[str, Union[Sequence[ScalarFloat], tf.Tensor]],
):
"""A private helper that validates the shapes of scalarization parameters.
Every scalarization parameter in the input dictionary is either a 1-D tensor
or `Sequence`, o... | A private helper that validates the shapes of scalarization parameters.
Every scalarization parameter in the input dictionary is either a 1-D tensor
or `Sequence`, or a tensor whose shape matches the shape of the input
`multi_objectives` tensor. This is invoked by the `Scalarizer.call` method.
Args:
multi... | _validate_scalarization_parameter_shape | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def __init__(self, num_of_objectives: int):
"""Initialize the Scalarizer.
Args:
num_of_objectives: A non-negative integer indicating the number of
objectives to scalarize.
Raises:
ValueError: if `not isinstance(num_of_objectives, int)`.
ValueError: if `num_of_objectives < 2`.
... | Initialize the Scalarizer.
Args:
num_of_objectives: A non-negative integer indicating the number of
objectives to scalarize.
Raises:
ValueError: if `not isinstance(num_of_objectives, int)`.
ValueError: if `num_of_objectives < 2`.
| __init__ | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def __call__(self, multi_objectives: tf.Tensor) -> tf.Tensor:
"""Returns a single reward by scalarizing multiple objectives.
Args:
multi_objectives: A `Tensor` of shape [batch_size, number_of_objectives],
where each column represents an objective.
Returns: A `Tensor` of shape [batch_size] re... | Returns a single reward by scalarizing multiple objectives.
Args:
multi_objectives: A `Tensor` of shape [batch_size, number_of_objectives],
where each column represents an objective.
Returns: A `Tensor` of shape [batch_size] representing scalarized rewards.
Raises:
ValueError: if `mul... | __call__ | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def _validate_scalarization_parameters(self, params: Dict[str, tf.Tensor]):
"""Validates the scalarization parameters.
Each scalarization parameter in the input dictionary should be a rank-2
tensor, and the last dimension size should match `self._num_of_objectives`.
Args:
params: A dictionary fr... | Validates the scalarization parameters.
Each scalarization parameter in the input dictionary should be a rank-2
tensor, and the last dimension size should match `self._num_of_objectives`.
Args:
params: A dictionary from parameter names to parameter tensors.
Raises:
ValueError: if any inpu... | _validate_scalarization_parameters | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def __init__(
self,
weights: Sequence[ScalarFloat],
multi_objective_transform: Optional[
Callable[[tf.Tensor], tf.Tensor]
] = None,
):
"""Initialize the LinearScalarizer.
Args:
weights: A `Sequence` of weights for linearly combining the objectives.
multi_objectiv... | Initialize the LinearScalarizer.
Args:
weights: A `Sequence` of weights for linearly combining the objectives.
multi_objective_transform: A `Optional` `Callable` that takes in a
`tf.Tensor` of multiple objective values and applies an arbitrary
transform that returns a `tf.Tensor` of tra... | __init__ | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def set_parameters(self, weights: tf.Tensor): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
"""Set the scalarization parameter of the LinearScalarizer.
Args:
weights: A a rank-2 `tf.Tensor` of weights shaped as [batch_size,
self._num_of_objectives], where `batch_size`... | Set the scalarization parameter of the LinearScalarizer.
Args:
weights: A a rank-2 `tf.Tensor` of weights shaped as [batch_size,
self._num_of_objectives], where `batch_size` should match the batch size
of the `multi_objectives` passed to the scalarizer call.
Raises:
ValueError: if ... | set_parameters | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def __init__(
self,
weights: Sequence[ScalarFloat],
reference_point: Sequence[ScalarFloat],
):
"""Initialize the ChebyshevScalarizer.
Args:
weights: A `Sequence` of weights.
reference_point: A `Sequence` of coordinates for the reference point.
Raises:
ValueError: if `... | Initialize the ChebyshevScalarizer.
Args:
weights: A `Sequence` of weights.
reference_point: A `Sequence` of coordinates for the reference point.
Raises:
ValueError: if `len(weights) != len(reference_point)`.
| __init__ | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def set_parameters(self, weights: tf.Tensor, reference_point: tf.Tensor): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
"""Set the scalarization parameters for the ChebyshevScalarizer.
Args:
weights: A rank-2 `tf.Tensor` of weights shaped as [batch_size,
self._num_of_... | Set the scalarization parameters for the ChebyshevScalarizer.
Args:
weights: A rank-2 `tf.Tensor` of weights shaped as [batch_size,
self._num_of_objectives], where `batch_size` should match the batch size
of the `multi_objectives` passed to the scalarizer call.
reference_point: A `tf.Te... | set_parameters | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def __init__(
self,
direction: Sequence[ScalarFloat],
transform_params: Sequence[PARAMS],
multi_objective_transform: Optional[
Callable[
[tf.Tensor, Sequence[ScalarFloat], Sequence[ScalarFloat]],
tf.Tensor,
]
] = None,
):
"""Initialize ... | Initialize the HyperVolumeScalarizer.
Args:
direction: A `Sequence` representing a directional vector, which will be
normalized to have unit length. Coordinates of the normalized direction
whose absolute values are less than `HyperVolumeScalarizer.ALMOST_ZERO`
will be considered zeros... | __init__ | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def set_parameters(
self,
direction: tf.Tensor, # pytype: disable=signature-mismatch # overriding-parameter-count-checks
transform_params: Dict[str, tf.Tensor],
):
"""Set the scalarization parameters for the HyperVolumeScalarizer.
Args:
direction: A `tf.Tensor` representing a direct... | Set the scalarization parameters for the HyperVolumeScalarizer.
Args:
direction: A `tf.Tensor` representing a directional vector, which will be
normalized to have unit length. Coordinates of the normalized direction
whose absolute values are less than `HyperVolumeScalarizer.ALMOST_ZERO`
... | set_parameters | python | tensorflow/agents | tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/multi_objective/multi_objective_scalarizer.py | Apache-2.0 |
def _remove_num_actions_dim_from_spec(
observation_spec: types.NestedTensorSpec,
) -> types.NestedTensorSpec:
"""Removes the extra `num_actions` dimension from the observation spec."""
obs_spec_no_num_actions = {
bandit_spec_utils.GLOBAL_FEATURE_KEY: observation_spec[
bandit_spec_utils.GLOBAL_FE... | Removes the extra `num_actions` dimension from the observation spec. | _remove_num_actions_dim_from_spec | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def create_feed_forward_common_tower_network(
observation_spec: types.NestedTensorSpec,
global_layers: Sequence[int],
arm_layers: Sequence[int],
common_layers: Sequence[int],
output_dim: int = 1,
global_preprocessing_combiner: Optional[Callable[..., types.Tensor]] = None,
arm_preprocessing_c... | Creates a common tower network with feedforward towers.
The network produced by this function can be used either in
`GreedyRewardPredictionPolicy`, or `NeuralLinUCBPolicy`.
In the former case, the network must have `output_dim=1`, it is going to be an
instance of `QNetwork`, and used in the policy as a reward ... | create_feed_forward_common_tower_network | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def create_feed_forward_dot_product_network(
observation_spec: types.NestedTensorSpec,
global_layers: Sequence[int],
arm_layers: Sequence[int],
activation_fn: Callable[
[types.Tensor], types.Tensor
] = tf.keras.activations.relu,
) -> types.Network:
"""Creates a dot product network with fee... | Creates a dot product network with feedforward towers.
Args:
observation_spec: A nested tensor spec containing the specs for global as
well as per-arm observations.
global_layers: Iterable of ints. Specifies the layers of the global tower.
arm_layers: Iterable of ints. Specifies the layers of the a... | create_feed_forward_dot_product_network | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def __init__(
self,
observation_spec: types.NestedTensorSpec,
global_network: types.Network,
arm_network: types.Network,
common_network: types.Network,
name='GlobalAndArmCommonTowerNetwork',
) -> types.Network:
"""Initializes an instance of `GlobalAndArmCommonTowerNetwork`.
... | Initializes an instance of `GlobalAndArmCommonTowerNetwork`.
The network architecture contains networks for both the global and the arm
features. The outputs of these networks are concatenated and led through a
third (common) network which in turn outputs reward estimates.
Args:
observation_spec... | __init__ | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def call(self, observation, step_type=None, network_state=()):
"""Runs the observation through the network."""
global_obs = observation[bandit_spec_utils.GLOBAL_FEATURE_KEY]
arm_obs = observation[bandit_spec_utils.PER_ARM_FEATURE_KEY]
arm_output, arm_state = self._arm_network(
arm_obs, step_typ... | Runs the observation through the network. | call | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def __init__(
self,
observation_spec: types.NestedTensorSpec,
global_network: types.Network,
arm_network: types.Network,
name: Optional[Text] = 'GlobalAndArmDotProductNetwork',
):
"""Initializes an instance of `GlobalAndArmDotProductNetwork`.
The network architecture contains ne... | Initializes an instance of `GlobalAndArmDotProductNetwork`.
The network architecture contains networks for both the global and the arm
features. The reward estimates will be the dot product of the global and per
arm outputs.
Args:
observation_spec: The observation spec for the policy that uses t... | __init__ | python | tensorflow/agents | tf_agents/bandits/networks/global_and_arm_feature_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/global_and_arm_feature_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec: types.NestedTensorSpec,
action_spec: types.NestedTensorSpec,
preprocessing_layers: Optional[Callable[..., types.Tensor]] = None,
preprocessing_combiner: Optional[Callable[..., types.Tensor]] = None,
conv_layer_params: Optional[Sequence[Any]] = N... | Creates an instance of `HeteroscedasticQNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input observations.
action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
actions.
preprocessing_layers: (Optional.) A nest of `tf.keras... | __init__ | python | tensorflow/agents | tf_agents/bandits/networks/heteroscedastic_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/heteroscedastic_q_network.py | Apache-2.0 |
def call(self, observation, step_type=None, network_state=()):
"""Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_state: A state tu... | Runs the given observation through the network.
Args:
observation: The observation to provide to the network.
step_type: The step type for the given observation. See `StepType` in
time_step.py.
network_state: A state tuple to pass to the network, mainly used by RNNs.
Returns:
A... | call | python | tensorflow/agents | tf_agents/bandits/networks/heteroscedastic_q_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/heteroscedastic_q_network.py | Apache-2.0 |
def testVarianceBoundaryConditions(self):
"""Tests that min/max variance conditions are satisfied."""
batch_size = 3
num_state_dims = 5
min_variance = 1.0
max_variance = 2.0
eps = 0.0001
states = tf.random.uniform([batch_size, num_state_dims])
network = heteroscedastic_q_network.Heterosc... | Tests that min/max variance conditions are satisfied. | testVarianceBoundaryConditions | python | tensorflow/agents | tf_agents/bandits/networks/heteroscedastic_q_network_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/networks/heteroscedastic_q_network_test.py | Apache-2.0 |
def _distribution(self, time_step, policy_state):
"""Implementation of `distribution`. Returns a `Categorical` distribution.
The returned `Categorical` distribution has (unnormalized) probabilities
`exp(inverse_temperature * weights)`.
Args:
time_step: A `TimeStep` tuple corresponding to `time_s... | Implementation of `distribution`. Returns a `Categorical` distribution.
The returned `Categorical` distribution has (unnormalized) probabilities
`exp(inverse_temperature * weights)`.
Args:
time_step: A `TimeStep` tuple corresponding to `time_step_spec()`.
policy_state: Unused in `CategoricalPo... | _distribution | python | tensorflow/agents | tf_agents/bandits/policies/categorical_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/categorical_policy.py | Apache-2.0 |
def testInverseTempUpdate(self, observation_shape, weights, seed):
"""Test that temperature updates perform as expected as it is increased."""
observation_spec = tensor_spec.TensorSpec(
shape=observation_shape, dtype=tf.float32, name='observation_spec'
)
time_step_spec = time_step.time_step_spec... | Test that temperature updates perform as expected as it is increased. | testInverseTempUpdate | python | tensorflow/agents | tf_agents/bandits/policies/categorical_policy_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/categorical_policy_test.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
name: Optional[Text] = None,
):
"""Initialization of the BaseConstraint class.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTenso... | Initialization of the BaseConstraint class.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
name: Python str name of this constraint.
| __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
constraint_network: Optional[types.Network],
error_loss_fn: types.LossFn = tf.compat.v1.losses.mean_squared_error,
name: Optional[Text] = 'NeuralConstraint',
):
"""Creates a trainable cons... | Creates a trainable constraint using a neural network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
constraint_network: An instance of `tf_agents.network.Network` used to
provide estimates of actio... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def compute_loss(
self,
observations: types.NestedTensor,
actions: types.NestedTensor,
rewards: types.Tensor,
weights: Optional[types.Float] = None,
training: bool = False,
) -> types.Tensor:
"""Computes loss for training the constraint network.
Args:
observations: A... | Computes loss for training the constraint network.
Args:
observations: A batch of observations.
actions: A batch of actions.
rewards: A batch of rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights. The output batch loss will be scaled by these weig... | compute_loss | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
constraint_network: types.Network,
error_loss_fn: types.LossFn = tf.compat.v1.losses.mean_squared_error,
comparator_fn: types.ComparatorFn = tf.greater,
margin: float = 0.0,
baseline... | Creates a trainable relative constraint using a neural network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
constraint_network: An instance of `tf_agents.network.Network` used to
provide estimates... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
constraint_network: types.Network,
error_loss_fn: types.LossFn = tf.compat.v1.losses.mean_squared_error,
comparator_fn: types.ComparatorFn = tf.greater,
absolute_value: float = 0.0,
... | Creates a trainable absolute constraint using a neural network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
constraint_network: An instance of `tf_agents.network.Network` used to
provide estimates... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
constraint_network: types.Network,
quantile: float = 0.5,
comparator_fn: types.ComparatorFn = tf.greater,
quantile_value: float = 0.0,
name: Text = 'QuantileConstraint',
):
"""... | Creates a trainable quantile constraint using a neural network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
constraint_network: An instance of `tf_agents.network.Network` used to
provide estimates... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
constraint_network: types.Network,
quantile: float = 0.5,
comparator_fn: types.ComparatorFn = tf.greater,
baseline_action_fn: Optional[
Callable[[types.Tensor], types.Tensor]
... | Creates a trainable relative quantile constraint using a neural network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
constraint_network: An instance of `tf_agents.network.Network` used to
provide ... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
input_network: Optional[types.Network] = None,
name: Optional[Text] = 'InputNetworkConstraint',
):
"""Creates a constraint using an input network.
Args:
time_step_spec: A `TimeStep` s... | Creates a constraint using an input network.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
input_network: An instance of `tf_agents.network.Network` used to provide
estimates of action feasibility.
... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def compute_feasibility_probability(
observation: types.NestedTensor,
constraints: Iterable[BaseConstraint],
batch_size: types.Int,
num_actions: int,
action_mask: Optional[types.Tensor] = None,
) -> types.Float:
"""Helper function to compute the action feasibility probability."""
feasibility_pro... | Helper function to compute the action feasibility probability. | compute_feasibility_probability | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def construct_mask_from_multiple_sources(
observation: types.NestedTensor,
observation_and_action_constraint_splitter: types.Splitter,
constraints: Iterable[BaseConstraint],
max_num_actions: int,
) -> Optional[types.Tensor]:
"""Constructs an action mask from multiple sources.
The sources include:
... | Constructs an action mask from multiple sources.
The sources include:
-- The action mask encoded in the observation,
-- the `num_actions` feature restricting the number of actions per sample,
-- the feasibility mask implied by constraints.
The resulting mask disables all actions that are masked out in any o... | construct_mask_from_multiple_sources | python | tensorflow/agents | tf_agents/bandits/policies/constraints.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints.py | Apache-2.0 |
def get_number_of_trainable_elements(network: types.Network) -> types.Float:
"""Gets the total # of elements in the network's trainable variables.
Args:
network: A `types.Network`.
Returns:
The total number of elements in the network's trainable variables.
"""
num_elements_list = []
for var in net... | Gets the total # of elements in the network's trainable variables.
Args:
network: A `types.Network`.
Returns:
The total number of elements in the network's trainable variables.
| get_number_of_trainable_elements | python | tensorflow/agents | tf_agents/bandits/policies/falcon_reward_prediction_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/falcon_reward_prediction_policy.py | Apache-2.0 |
def _find_action_probabilities(
greedy_action_prob: types.Tensor,
other_actions_probs: types.Tensor,
max_exploration_prob: float,
):
"""Finds action probabilities satisfying `max_exploration_prob`.
Given action probabilities calculated by different values of the gamma
parameter, this function attempt... | Finds action probabilities satisfying `max_exploration_prob`.
Given action probabilities calculated by different values of the gamma
parameter, this function attempts to find action probabilities at a specific
gamma value such that non-greedy actions are chosen with at most
`max_exploration_prob` probability. ... | _find_action_probabilities | python | tensorflow/agents | tf_agents/bandits/policies/falcon_reward_prediction_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/falcon_reward_prediction_policy.py | Apache-2.0 |
def _get_number_of_allowed_actions(
self, mask: Optional[types.Tensor]
) -> types.Float:
"""Gets the number of allowed actions.
Args:
mask: An optional mask represented by a tensor shaped as [batch_size,
num_actions].
Returns:
The number of allowed actions. It can be either a s... | Gets the number of allowed actions.
Args:
mask: An optional mask represented by a tensor shaped as [batch_size,
num_actions].
Returns:
The number of allowed actions. It can be either a scalar (when `mask` is
None), or a tensor shaped as [batch_size].
| _get_number_of_allowed_actions | python | tensorflow/agents | tf_agents/bandits/policies/falcon_reward_prediction_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/falcon_reward_prediction_policy.py | Apache-2.0 |
def _compute_gamma(
self, mask: Optional[types.Tensor], dtype: tf.DType, batch_size: int
) -> types.Float:
"""Computes the gamma parameter(s) in the sampling probability.
This helper method implements a simple heuristic for computing the
the gamma parameter in Step 2 of Algorithm 1 in the paper
... | Computes the gamma parameter(s) in the sampling probability.
This helper method implements a simple heuristic for computing the
the gamma parameter in Step 2 of Algorithm 1 in the paper
https://arxiv.org/pdf/2003.12699.pdf. A higher gamma makes the action
sampling distribution concentrate more on the g... | _compute_gamma | python | tensorflow/agents | tf_agents/bandits/policies/falcon_reward_prediction_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/falcon_reward_prediction_policy.py | Apache-2.0 |
def scalarize_objectives(
objectives_tensor: tf.Tensor,
scalarizer: multi_objective_scalarizer.Scalarizer,
):
"""Scalarize a rank-3 objectives tensor into a rank-2 tensor.
Scalarize an objective values tensor shaped as
[batch_size, num_of_objectives, num_of_actions] along the second dimension
into a ra... | Scalarize a rank-3 objectives tensor into a rank-2 tensor.
Scalarize an objective values tensor shaped as
[batch_size, num_of_objectives, num_of_actions] along the second dimension
into a rank-2 tensor shaped as [batch_size, num_of_actions]
Args:
objectives_tensor: An objectives tensor to be scalarized.
... | scalarize_objectives | python | tensorflow/agents | tf_agents/bandits/policies/greedy_multi_objective_neural_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/greedy_multi_objective_neural_policy.py | Apache-2.0 |
def _predict(
self,
observation: types.NestedSpecTensorOrArray,
step_type: types.SpecTensorOrArray,
policy_state: Sequence[types.TensorSpec],
) -> Tuple[tf.Tensor, List[types.TensorSpec]]:
"""Predict the objectives using the policy's objective networks.
Args:
observation: The ob... | Predict the objectives using the policy's objective networks.
Args:
observation: The observation whose objectives are to be predicted.
step_type: The `tf_agents.trajectories.time_step.StepType` for the input
observation.
policy_state: The states for the policy's objective networks.
R... | _predict | python | tensorflow/agents | tf_agents/bandits/policies/greedy_multi_objective_neural_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/greedy_multi_objective_neural_policy.py | Apache-2.0 |
def _action_distribution(self, mask, predicted_rewards):
"""Returns the action with largest predicted reward."""
# Argmax.
batch_size = tf.shape(predicted_rewards)[0]
if mask is not None:
actions = policy_utilities.masked_argmax(
predicted_rewards, mask, output_type=self.action_spec.dtyp... | Returns the action with largest predicted reward. | _action_distribution | python | tensorflow/agents | tf_agents/bandits/policies/greedy_reward_prediction_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/greedy_reward_prediction_policy.py | Apache-2.0 |
def conjugate_gradient(
a_mat: types.Tensor, b_mat: types.Tensor, tol: float = 1e-10
) -> types.Float:
"""Returns `X` such that `A * X = B`.
Implements the Conjugate Gradient method.
https://en.wikipedia.org/wiki/Conjugate_gradient_method
Args:
a_mat: a Symmetric Positive Definite matrix, represented ... | Returns `X` such that `A * X = B`.
Implements the Conjugate Gradient method.
https://en.wikipedia.org/wiki/Conjugate_gradient_method
Args:
a_mat: a Symmetric Positive Definite matrix, represented as a `Tensor` of
shape `[n, n]`.
b_mat: a `Tensor` of shape `[n, k]`.
tol: (float) desired toleran... | conjugate_gradient | python | tensorflow/agents | tf_agents/bandits/policies/linalg.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linalg.py | Apache-2.0 |
def while_exit_cond(i, x, p, r, rs_old, rs_new):
"""Exit the loop when n is reached or when the residual becomes small."""
del x # unused
del p # unused
del r # unused
del rs_old # unused
i_cond = tf.less(i, n)
residual_cond = tf.greater(tf.reduce_max(tf.sqrt(rs_new)), tol)
return tf... | Exit the loop when n is reached or when the residual becomes small. | while_exit_cond | python | tensorflow/agents | tf_agents/bandits/policies/linalg.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linalg.py | Apache-2.0 |
def simplified_woodbury_update(
a_inv: types.Float, u: types.Float
) -> types.Float:
"""Returns `w` such that `inverse(a + u.T.dot(u)) = a_inv + w`.
Makes use of the Woodbury matrix identity. See
https://en.wikipedia.org/wiki/Woodbury_matrix_identity.
**NOTE**: This implementation assumes that a_inv is sy... | Returns `w` such that `inverse(a + u.T.dot(u)) = a_inv + w`.
Makes use of the Woodbury matrix identity. See
https://en.wikipedia.org/wiki/Woodbury_matrix_identity.
**NOTE**: This implementation assumes that a_inv is symmetric. Since it's too
expensive to check symmetricity, the function silently outputs a wro... | simplified_woodbury_update | python | tensorflow/agents | tf_agents/bandits/policies/linalg.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linalg.py | Apache-2.0 |
def update_inverse(a_inv: types.Float, x: types.Float) -> types.Float:
"""Updates the inverse using the Woodbury matrix identity.
Given a matrix `A` of size d-by-d and a matrix `X` of size k-by-d, this
function computes the inverse of B = A + X^T X, assuming that the inverse of
A is available.
Reference:
... | Updates the inverse using the Woodbury matrix identity.
Given a matrix `A` of size d-by-d and a matrix `X` of size k-by-d, this
function computes the inverse of B = A + X^T X, assuming that the inverse of
A is available.
Reference:
https://en.wikipedia.org/wiki/Woodbury_matrix_identity
Args:
a_inv: a... | update_inverse | python | tensorflow/agents | tf_agents/bandits/policies/linalg.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linalg.py | Apache-2.0 |
def _predict_mean_reward(
self,
theta: tf.Tensor,
global_observation: tf.Tensor,
arm_observations: Optional[tf.Tensor],
) -> tf.Tensor:
"""Predicts the mean reward using the theta vectors.
Args:
theta: A `tf.Tensor` of theta vectors, shaped as [num_models,
overall_contex... | Predicts the mean reward using the theta vectors.
Args:
theta: A `tf.Tensor` of theta vectors, shaped as [num_models,
overall_context_dim].
global_observation: A `tf.Tensor` shaped as [batch_size,
self._global_context_dim].
arm_observations: An optional `tf.Tensor` shaped as [batc... | _predict_mean_reward | python | tensorflow/agents | tf_agents/bandits/policies/linear_bandit_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linear_bandit_policy.py | Apache-2.0 |
def _predict_mean_reward_and_variance(
self, global_observation: tf.Tensor, arm_observations: Optional[tf.Tensor]
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Predicts mean reward and variance.
Args:
global_observation: A `tf.Tensor` shaped as [batch_size,
self._global_context_dim].
arm_obs... | Predicts mean reward and variance.
Args:
global_observation: A `tf.Tensor` shaped as [batch_size,
self._global_context_dim].
arm_observations: An optional `tf.Tensor` shaped as [batch_size,
self._num_actions, self._arm_context_dim]. Expected to be supplied only
when self._accept... | _predict_mean_reward_and_variance | python | tensorflow/agents | tf_agents/bandits/policies/linear_bandit_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linear_bandit_policy.py | Apache-2.0 |
def _get_current_observation(
self, global_observation, arm_observations, arm_index
):
"""Helper function to construct the observation for a specific arm.
This function constructs the observation depending if the policy accepts
per-arm features or not. If not, it simply returns the original observa... | Helper function to construct the observation for a specific arm.
This function constructs the observation depending if the policy accepts
per-arm features or not. If not, it simply returns the original observation.
If yes, it concatenates the global observation with the observation of the
arm indexed b... | _get_current_observation | python | tensorflow/agents | tf_agents/bandits/policies/linear_bandit_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linear_bandit_policy.py | Apache-2.0 |
def _split_observation(self, observation):
"""Splits the observation into global and arm observations."""
if self._accepts_per_arm_features:
global_observation = observation[bandit_spec_utils.GLOBAL_FEATURE_KEY]
arm_observations = observation[bandit_spec_utils.PER_ARM_FEATURE_KEY]
if not arm_o... | Splits the observation into global and arm observations. | _split_observation | python | tensorflow/agents | tf_agents/bandits/policies/linear_bandit_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/linear_bandit_policy.py | Apache-2.0 |
def pinball_loss(
y_true: types.Tensor,
y_pred: types.Tensor,
weights: types.Float = 1.0,
scope: Optional[Text] = None,
loss_collection: tf.compat.v1.GraphKeys = tf.compat.v1.GraphKeys.LOSSES,
reduction: tf.compat.v1.losses.Reduction = tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
qu... | Adds a Pinball loss for quantile regression.
```
loss = quantile * (y_true - y_pred) if y_true > y_pred
loss = (quantile - 1) * (y_true - y_pred) otherwise
```
See: https://en.wikipedia.org/wiki/Quantile_regression#Quantiles
`weights` acts as a coefficient for the loss. If a scalar is provid... | pinball_loss | python | tensorflow/agents | tf_agents/bandits/policies/loss_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/loss_utils.py | Apache-2.0 |
def __init__(
self,
mixture_distribution: types.Distribution,
policies: Sequence[tf_policy.TFPolicy],
name: Optional[Text] = None,
):
"""Initializes an instance of `MixturePolicy`.
Args:
mixture_distribution: A `tfd.Categorical` distribution on the domain `[0,
len(polici... | Initializes an instance of `MixturePolicy`.
Args:
mixture_distribution: A `tfd.Categorical` distribution on the domain `[0,
len(policies) -1]`. This distribution is used by the mixture policy to
choose which policy to listen to.
policies: List of TF Policies. These are the policies that... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/mixture_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/mixture_policy.py | Apache-2.0 |
def _get_predicted_rewards_from_linucb(self, observation_numpy, batch_size):
"""Runs one step of LinUCB using numpy arrays."""
observation_numpy.reshape([batch_size, self._encoding_dim])
predicted_rewards = []
for k in range(self._num_actions):
a_inv = np.linalg.inv(self._a_numpy[k] + np.eye(self... | Runs one step of LinUCB using numpy arrays. | _get_predicted_rewards_from_linucb | python | tensorflow/agents | tf_agents/bandits/policies/neural_linucb_policy_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/neural_linucb_policy_test.py | Apache-2.0 |
def __init__(
self,
features: types.Tensor,
num_slots: int,
logits: types.Tensor,
penalty_mixture_coefficient: float = 1.0,
):
"""Initializes an instance of PenalizedPlackettLuce.
Args:
features: Item features based on which similarity is calculated.
num_slots: The n... | Initializes an instance of PenalizedPlackettLuce.
Args:
features: Item features based on which similarity is calculated.
num_slots: The number of slots to fill: this many items will be sampled.
logits: Unnormalized log probabilities for the PlackettLuce distribution.
Shape is `[num_items]... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/ranking_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/ranking_policy.py | Apache-2.0 |
def _penalizer_fn(
self,
logits: types.Float,
slots: tf.Tensor,
num_slotted: tf.Tensor,
):
"""Downscores items by their similarity to already selected items.
Args:
logits: The current logits of all items, shaped as [batch_size,
num_items].
slots: A tensor of indice... | Downscores items by their similarity to already selected items.
Args:
logits: The current logits of all items, shaped as [batch_size,
num_items].
slots: A tensor of indices of the selected items, shaped as [batch_size,
num_slots]. Only the first `num_slotted` columns correspond to valid... | _penalizer_fn | python | tensorflow/agents | tf_agents/bandits/policies/ranking_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/ranking_policy.py | Apache-2.0 |
def __init__(
self,
features: types.Tensor,
num_slots: int,
logits: types.Tensor,
penalty_mixture_coefficient: float = 1.0,
):
"""Initializes an instance of CosinePenalizedPlackettLuce.
Args:
features: Item features based on which similarity is calculated.
num_slots:... | Initializes an instance of CosinePenalizedPlackettLuce.
Args:
features: Item features based on which similarity is calculated.
num_slots: The number of slots to fill: this many items will be sampled.
logits: Unnormalized log probabilities for the PlackettLuce distribution.
Shape is `[num_... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/ranking_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/ranking_policy.py | Apache-2.0 |
def __init__(
self,
features: types.Tensor,
num_slots: int,
logits: types.Tensor,
penalty_mixture_coefficient: float = 1.0,
):
"""Initializes an instance of NoPenaltyPlackettLuce.
Args:
features: Unused for this distribution.
num_slots: The number of slots to fill: t... | Initializes an instance of NoPenaltyPlackettLuce.
Args:
features: Unused for this distribution.
num_slots: The number of slots to fill: this many items will be sampled.
logits: Unnormalized log probabilities for the PlackettLuce distribution.
Shape is `[num_items]`.
penalty_mixture_... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/ranking_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/ranking_policy.py | Apache-2.0 |
def __init__(
self,
num_items: int,
num_slots: int,
time_step_spec: types.TimeStep,
network: types.Network,
item_sampler: tfd.Distribution,
penalty_mixture_coefficient: float = 1.0,
logits_temperature: float = 1.0,
name: Optional[Text] = None,
):
"""Initialize... | Initializes an instance of `RankingPolicy`.
Args:
num_items: The number of items the policy can choose from, to be slotted.
num_slots: The number of recommendation slots presented to the user, i.e.,
chosen by the policy.
time_step_spec: The time step spec.
network: The network that ... | __init__ | python | tensorflow/agents | tf_agents/bandits/policies/ranking_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/ranking_policy.py | Apache-2.0 |
def _action_distribution(
self, mask: Optional[types.Tensor], predicted_rewards: types.Tensor
) -> Tuple[tfp.distributions.Distribution, types.Tensor]:
"""Returns an action distribution based on predicted rewards.
Sub-classes are expected to implement this method.
Args:
mask: A 2-D tensor of... | Returns an action distribution based on predicted rewards.
Sub-classes are expected to implement this method.
Args:
mask: A 2-D tensor of binary masks shaped as [batch_size, num_of_arms].
predicted_rewards: A 2-D tensor of predicted rewards shaped as
[batch_size, num_of_arms].
Returns... | _action_distribution | python | tensorflow/agents | tf_agents/bandits/policies/reward_prediction_base_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/reward_prediction_base_policy.py | Apache-2.0 |
def _maybe_save_chosen_arm_features(
self,
time_step: ts.TimeStep,
action: types.Tensor,
step: policy_step.PolicyStep,
) -> policy_step.PolicyStep:
"""Extracts and saves the chosen arm features in the policy info.
If the policy accepts arm features, this method extracts the arm featur... | Extracts and saves the chosen arm features in the policy info.
If the policy accepts arm features, this method extracts the arm features
from `time_step.observation` corresponding to the input `action` tensor,
saves it in the policy info of the input `step` and returns the modified
step. Otherwise, the... | _maybe_save_chosen_arm_features | python | tensorflow/agents | tf_agents/bandits/policies/reward_prediction_base_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/reward_prediction_base_policy.py | Apache-2.0 |
def _action(
self,
time_step: ts.TimeStep,
policy_state: types.NestedTensor,
seed: Optional[types.Seed] = None,
) -> policy_step.PolicyStep:
"""Implementation of `action`.
Args:
time_step: A `TimeStep` tuple corresponding to `time_step_spec()`.
policy_state: A Tensor, or a... | Implementation of `action`.
Args:
time_step: A `TimeStep` tuple corresponding to `time_step_spec()`.
policy_state: A Tensor, or a nested dict, list or tuple of Tensors
representing the previous policy_state.
seed: Seed to use if action performs sampling (optional).
Returns:
A `... | _action | python | tensorflow/agents | tf_agents/bandits/policies/reward_prediction_base_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/reward_prediction_base_policy.py | Apache-2.0 |
def get_distribution_strategy(
distribution_strategy="default", num_gpus=0, num_packs=-1
):
"""Return a DistributionStrategy for running the model.
Args:
distribution_strategy: a string specifying which distribution strategy to
use. Accepted values are 'off', 'default', 'one_device', and 'mirrored'
... | Return a DistributionStrategy for running the model.
Args:
distribution_strategy: a string specifying which distribution strategy to
use. Accepted values are 'off', 'default', 'one_device', and 'mirrored'
case insensitive. 'off' means not to use Distribution Strategy; 'default'
means to choose ... | get_distribution_strategy | python | tensorflow/agents | tf_agents/benchmark/distribution_strategy_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/distribution_strategy_utils.py | Apache-2.0 |
def benchmark_pong_v0_at_3M(self):
"""Benchmarks to 3M Env steps.
This is below the 12.5M train steps (50M frames) run by the paper to
converge. Running 12.5M at the current throughput would take more than a
week. 1-2 days is the max duration for a remotely usable test. 3M only
confirms we have not... | Benchmarks to 3M Env steps.
This is below the 12.5M train steps (50M frames) run by the paper to
converge. Running 12.5M at the current throughput would take more than a
week. 1-2 days is the max duration for a remotely usable test. 3M only
confirms we have not regressed at 3M and does not gurantee con... | benchmark_pong_v0_at_3M | python | tensorflow/agents | tf_agents/benchmark/dqn_benchmark.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/dqn_benchmark.py | Apache-2.0 |
def _run(
self,
strategy,
batch_size=64,
tf_function=True,
replay_buffer_max_length=1000,
train_steps=110,
log_steps=10,
):
"""Runs Dqn CartPole environment.
Args:
strategy: Strategy to use, None is a valid value.
batch_size: Total batch size to use for t... | Runs Dqn CartPole environment.
Args:
strategy: Strategy to use, None is a valid value.
batch_size: Total batch size to use for the run.
tf_function: If True tf.function is used.
replay_buffer_max_length: Max length of the replay buffer.
train_steps: Number of steps to run.
log_s... | _run | python | tensorflow/agents | tf_agents/benchmark/dqn_benchmark_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/dqn_benchmark_test.py | Apache-2.0 |
def run_and_report(
self,
train_step,
strategy,
batch_size,
train_steps=110,
log_steps=10,
iterator=None,
):
"""Run function provided and report results per `tf.test.Benchmark`.
Args:
train_step: Function to execute on each step.
strategy: Strategy to use... | Run function provided and report results per `tf.test.Benchmark`.
Args:
train_step: Function to execute on each step.
strategy: Strategy to use, None is a valid value.
batch_size: Total batch_size.
train_steps: Number of steps to run.
log_steps: How often to log step statistics, e.g. ... | run_and_report | python | tensorflow/agents | tf_agents/benchmark/dqn_benchmark_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/dqn_benchmark_test.py | Apache-2.0 |
def __init__(self, output_dir=None):
"""Initialize class.
Args:
output_dir: Base directory to store all output for the test.
"""
# MLCompass sets this value, but PerfZero OSS passes it as an arg.
if os.getenv('BENCHMARK_OUTPUT_DIR'):
self.output_dir = os.getenv('BENCHMARK_OUTPUT_DIR')
... | Initialize class.
Args:
output_dir: Base directory to store all output for the test.
| __init__ | python | tensorflow/agents | tf_agents/benchmark/perfzero_benchmark.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/perfzero_benchmark.py | Apache-2.0 |
def setUp(self):
"""Sets up and resets flags before each test."""
logging.set_verbosity(logging.INFO)
if PerfZeroBenchmark.local_flags is None:
# Loads flags to get defaults to then override. List cannot be empty.
flags.FLAGS(['foo'])
saved_flag_values = flagsaver.save_flag_values()
... | Sets up and resets flags before each test. | setUp | python | tensorflow/agents | tf_agents/benchmark/perfzero_benchmark.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/perfzero_benchmark.py | Apache-2.0 |
def build_metric(
self,
name: str,
value: Number,
min_value: Optional[Number] = None,
max_value: Optional[Number] = None,
):
"""Builds a dictionary representing the metric to record.
Args:
name: Name of the metric.
value: Value of the metric.
min_value: Lowest ... | Builds a dictionary representing the metric to record.
Args:
name: Name of the metric.
value: Value of the metric.
min_value: Lowest acceptable value.
max_value: Highest acceptable value.
Returns:
Dictionary representing the metric.
| build_metric | python | tensorflow/agents | tf_agents/benchmark/perfzero_benchmark.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/perfzero_benchmark.py | Apache-2.0 |
def test_build_metric(self):
"""Tests building metric with only required values."""
bench = perfzero_benchmark.PerfZeroBenchmark()
metric_name = 'metric_name'
value = 25.93
expected_metric = {'name': metric_name, 'value': value}
metric = bench.build_metric(metric_name, value)
self.assertEqu... | Tests building metric with only required values. | test_build_metric | python | tensorflow/agents | tf_agents/benchmark/perfzero_benchmark_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/perfzero_benchmark_test.py | Apache-2.0 |
def test_build_metric_min_max(self):
"""Tests building metric with min and max values."""
bench = perfzero_benchmark.PerfZeroBenchmark()
metric_name = 'metric_name'
value = 25.93
min_value = 0.004
max_value = 59783
expected_metric = {
'name': metric_name,
'value': value,
... | Tests building metric with min and max values. | test_build_metric_min_max | python | tensorflow/agents | tf_agents/benchmark/perfzero_benchmark_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/perfzero_benchmark_test.py | Apache-2.0 |
def run_benchmark(self, training_env, expected_min, expected_max):
"""Run benchmark for a given environment.
In order to execute ~1M environment steps to match the paper, we run 489
iterations (num_iterations=489) which results in 1,001,472 environment
steps. Each iteration results in 320 training step... | Run benchmark for a given environment.
In order to execute ~1M environment steps to match the paper, we run 489
iterations (num_iterations=489) which results in 1,001,472 environment
steps. Each iteration results in 320 training steps and 2,048 environment
steps. Thus 489 * 2,048 = 1,001,472 environmen... | run_benchmark | python | tensorflow/agents | tf_agents/benchmark/ppo_benchmark.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/ppo_benchmark.py | Apache-2.0 |
def run_test(
target_call,
num_steps,
strategy,
batch_size=None,
log_steps=100,
num_steps_per_batch=1,
iterator=None,
):
"""Run benchmark and return TimeHistory object with stats.
Args:
target_call: Call to execute for each step.
num_steps: Number of steps to run.
strategy: ... | Run benchmark and return TimeHistory object with stats.
Args:
target_call: Call to execute for each step.
num_steps: Number of steps to run.
strategy: None or tf.distribute.DistibutionStrategy object.
batch_size: Total batch size.
log_steps: Interval of steps between logging of stats.
num_ste... | run_test | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def __init__(self, batch_size, log_steps, num_steps_per_batch=1):
"""Callback for logging performance.
Args:
batch_size: Total batch size.
log_steps: Interval of steps between logging of stats.
num_steps_per_batch: Number of steps per batch.
"""
self.batch_size = batch_size
super(... | Callback for logging performance.
Args:
batch_size: Total batch size.
log_steps: Interval of steps between logging of stats.
num_steps_per_batch: Number of steps per batch.
| __init__ | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def on_batch_end(self):
"""Records elapse time of the batch and calculates examples per second."""
if self.global_steps % self.log_steps == 0:
timestamp = time.time()
elapsed_time = timestamp - self.start_time
steps_per_second = self.log_steps / elapsed_time
examples_per_second = steps_p... | Records elapse time of the batch and calculates examples per second. | on_batch_end | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def get_average_examples_per_second(self, warmup=True):
"""Returns average examples per second so far.
Examples per second are defined by `batch_size` * `num_steps_per_batch`
Args:
warmup: If true ignore first set of steps executed as determined by
`log_steps`.
Returns:
Average ex... | Returns average examples per second so far.
Examples per second are defined by `batch_size` * `num_steps_per_batch`
Args:
warmup: If true ignore first set of steps executed as determined by
`log_steps`.
Returns:
Average examples per second.
| get_average_examples_per_second | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def get_average_step_time(self, warmup=True):
"""Returns average step time (seconds) so far.
Args:
warmup: If true ignore first set of steps executed as determined by
`log_steps`.
Returns:
Average step time in seconds.
"""
if warmup:
if len(self.timestamp_log) < 3:
... | Returns average step time (seconds) so far.
Args:
warmup: If true ignore first set of steps executed as determined by
`log_steps`.
Returns:
Average step time in seconds.
| get_average_step_time | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def get_variable_value(agent, name):
"""Returns the value of the trainable variable with the given name."""
policy_vars = agent.policy.variables()
tf_vars = [v for v in policy_vars if name in v.name]
assert tf_vars, 'Variable "{}" does not exist. Found: {}'.format(
name, policy_vars
)
if tf.executing_... | Returns the value of the trainable variable with the given name. | get_variable_value | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def find_event_log(
eventlog_dir: str, log_file_pattern: str = 'events.out.tfevents.*'
) -> str:
"""Find the event log in a given folder.
Expects to find a single log file matching the pattern provided.
Args:
eventlog_dir: Event log directory to search.
log_file_pattern: Pattern to use to find the e... | Find the event log in a given folder.
Expects to find a single log file matching the pattern provided.
Args:
eventlog_dir: Event log directory to search.
log_file_pattern: Pattern to use to find the event log.
Returns:
Path to the event log file that was found.
Raises:
FileNotFoundError: If ... | find_event_log | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def extract_event_log_values(
event_file: str,
event_tag: str,
end_step: Optional[int] = None,
start_step: Optional[int] = 0,
) -> Tuple[Dict[int, np.generic], float]:
"""Extracts the event values for the `event_tag` and total wall time.
Args:
event_file: Path to the event log.
event_tag: E... | Extracts the event values for the `event_tag` and total wall time.
Args:
event_file: Path to the event log.
event_tag: Event to extract from the logs.
end_step: If set, processing of the event log ends on this step.
start_step: First step to look for in event log, defaults to 0.
Returns:
Tuple... | extract_event_log_values | python | tensorflow/agents | tf_agents/benchmark/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils.py | Apache-2.0 |
def test_extract_value(self):
"""Tests extracting data from all steps in the event log."""
values, walltime = utils.extract_event_log_values(
os.path.join(TEST_DATA, 'event_log_3m/events.out.tfevents.1599310762'),
'AverageReturn',
)
# Verifies all (3M) records were examined 0-3M = 301.
... | Tests extracting data from all steps in the event log. | test_extract_value | python | tensorflow/agents | tf_agents/benchmark/utils_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils_test.py | Apache-2.0 |
def test_extract_value_1m_only(self):
"""Tests extracting data from the first 1M steps in the event log."""
values, walltime = utils.extract_event_log_values(
os.path.join(TEST_DATA, 'event_log_3m/events.out.tfevents.1599310762'),
'AverageReturn',
1000000,
)
# Verifies only 1M re... | Tests extracting data from the first 1M steps in the event log. | test_extract_value_1m_only | python | tensorflow/agents | tf_agents/benchmark/utils_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils_test.py | Apache-2.0 |
def test_extract_value_1m_only_start_at_1k(self):
"""Tests extracting data starting at step 1k."""
values, walltime = utils.extract_event_log_values(
os.path.join(TEST_DATA, 'event_log_3m/events.out.tfevents.1599310762'),
'AverageReturn',
1000000,
start_step=10000,
)
# Ve... | Tests extracting data starting at step 1k. | test_extract_value_1m_only_start_at_1k | python | tensorflow/agents | tf_agents/benchmark/utils_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/benchmark/utils_test.py | Apache-2.0 |
def __init__(
self,
temperature,
logits=None,
probs=None,
dtype=tf.int32,
validate_args=False,
allow_nan_stats=True,
name='GumbelSoftmax',
):
"""Initialize GumbelSoftmax using class log-probabilities.
Args:
temperature: A `Tensor`, representing the temper... | Initialize GumbelSoftmax using class log-probabilities.
Args:
temperature: A `Tensor`, representing the temperature of one or more
distributions. The temperature values must be positive, and the shape
must broadcast against `(logits or probs)[..., 0]`.
logits: An N-D `Tensor`, `N >= 1`,... | __init__ | python | tensorflow/agents | tf_agents/distributions/gumbel_softmax.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/gumbel_softmax.py | Apache-2.0 |
def __init__(
self,
logits,
mask,
probs=None,
dtype=tf.int32,
validate_args=False,
allow_nan_stats=True,
neg_inf=-1e10,
name='MaskedCategorical',
):
"""Initialize Categorical distributions using class log-probabilities.
Args:
logits: An N-D `Tensor`... | Initialize Categorical distributions using class log-probabilities.
Args:
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
set of Categorical distributions. The first `N - 1` dimensions index
into a batch of independent distributions and the last dimension
re... | __init__ | python | tensorflow/agents | tf_agents/distributions/masked.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/masked.py | Apache-2.0 |
def testCopy(self):
"""Confirm we can copy the distribution."""
distribution = masked.MaskedCategorical(
[100.0, 100.0, 100.0], mask=[True, False, True]
)
copy = distribution.copy()
with self.cached_session() as s:
probs_np = s.run(copy.probs_parameter())
logits_np = s.run(copy.l... | Confirm we can copy the distribution. | testCopy | python | tensorflow/agents | tf_agents/distributions/masked_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/masked_test.py | Apache-2.0 |
def sample(distribution, reparam=False, **kwargs):
"""Sample from distribution either with reparameterized sampling or regular sampling.
Args:
distribution: A `tfp.distributions.Distribution` instance.
reparam: Whether to use reparameterized sampling.
**kwargs: Parameters to be passed to distribution's... | Sample from distribution either with reparameterized sampling or regular sampling.
Args:
distribution: A `tfp.distributions.Distribution` instance.
reparam: Whether to use reparameterized sampling.
**kwargs: Parameters to be passed to distribution's sample() fucntion.
Returns:
| sample | python | tensorflow/agents | tf_agents/distributions/reparameterized_sampling.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/reparameterized_sampling.py | Apache-2.0 |
def __init__(
self,
logits=None,
probs=None,
dtype=tf.int32,
force_probs_to_zero_outside_support=False,
validate_args=False,
allow_nan_stats=True,
shift=None,
name="ShiftedCategorical",
):
"""Initialize Categorical distributions using class log-probabilities.
... | Initialize Categorical distributions using class log-probabilities.
Args:
logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities of a
set of Categorical distributions. The first `N - 1` dimensions index
into a batch of independent distributions and the last dimension
re... | __init__ | python | tensorflow/agents | tf_agents/distributions/shifted_categorical.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/shifted_categorical.py | Apache-2.0 |
def sample(self, sample_shape=(), seed=None, name="sample", **kwargs):
"""Generate samples of the specified shape."""
sample = super(ShiftedCategorical, self).sample(
sample_shape=sample_shape, seed=seed, name=name, **kwargs
)
return sample + self._shift | Generate samples of the specified shape. | sample | python | tensorflow/agents | tf_agents/distributions/shifted_categorical.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/shifted_categorical.py | Apache-2.0 |
def __init__(
self, distribution, spec, validate_args=False, name="SquashToSpecNormal"
):
"""Constructs a SquashToSpecNormal distribution.
Args:
distribution: input normal distribution with normalized mean and std dev
spec: bounded action spec from which to compute action ranges
valid... | Constructs a SquashToSpecNormal distribution.
Args:
distribution: input normal distribution with normalized mean and std dev
spec: bounded action spec from which to compute action ranges
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for val... | __init__ | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def kl_divergence(self, other, name="kl_divergence"):
"""Computes the KL Divergence between two SquashToSpecNormal distributions."""
if not isinstance(other, SquashToSpecNormal):
raise ValueError(
"other distribution should be of type "
"SquashToSpecNormal, got {}".format(other)
... | Computes the KL Divergence between two SquashToSpecNormal distributions. | kl_divergence | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def mode(self, name="mode"):
"""Compute mean of the SquashToSpecNormal distribution."""
mean = (
self.action_magnitudes * tf.tanh(self.input_distribution.mode())
+ self.action_means
)
return mean | Compute mean of the SquashToSpecNormal distribution. | mode | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def get_parameters(value: Any) -> Params:
"""Creates a recursive `Params` object from `value`.
The `Params` object can be converted back to an object of type `type(value)`
with `make_from_parameters`. For more details, see the docstring of
`Params`.
Args:
value: Typically a user provides `tfp.Distribut... | Creates a recursive `Params` object from `value`.
The `Params` object can be converted back to an object of type `type(value)`
with `make_from_parameters`. For more details, see the docstring of
`Params`.
Args:
value: Typically a user provides `tfp.Distribution`, `tfp.Bijector`, or
`tf.linalg.Linea... | get_parameters | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def make_from_parameters(value: Params) -> Any:
"""Creates an instance of type `value.type_` with the parameters in `value`.
For more details, see the docstrings for `get_parameters` and `Params`.
This function may raise strange errors if `value` is a `Params` created from
a badly constructed object (one whic... | Creates an instance of type `value.type_` with the parameters in `value`.
For more details, see the docstrings for `get_parameters` and `Params`.
This function may raise strange errors if `value` is a `Params` created from
a badly constructed object (one which does not set `self._parameters`
properly). For e... | make_from_parameters | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def merge_to_parameters_from_dict(
value: Params, params_dict: Mapping[Text, Any]
) -> Params:
"""Merges dict matching data of `parameters_to_dict(value)` to a new `Params`.
For more details, see the example below and the documentation of
`parameters_to_dict`.
Example:
```python
scale_matrix = tf.Var... | Merges dict matching data of `parameters_to_dict(value)` to a new `Params`.
For more details, see the example below and the documentation of
`parameters_to_dict`.
Example:
```python
scale_matrix = tf.Variable([[1.0, 2.0], [-1.0, 0.0]])
d = tfp.distributions.MultivariateNormalDiag(
loc=[1.0, 1.0], s... | merge_to_parameters_from_dict | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def __init__(
self, event_shape: tf.TensorShape, dtype: tf.DType, parameters: Params
):
"""Construct a `DistributionSpecV2` from a Distribution's properties.
Note that the `parameters` used to create the spec should contain
`tf.TypeSpec` objects instead of tensors. We check for this.
Args:
... | Construct a `DistributionSpecV2` from a Distribution's properties.
Note that the `parameters` used to create the spec should contain
`tf.TypeSpec` objects instead of tensors. We check for this.
Args:
event_shape: The distribution's `event_shape`. This is the shape that
`distribution.sample... | __init__ | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def assert_specs_are_compatible(
network_output_spec: types.NestedTensorSpec,
spec: types.NestedTensorSpec,
message_prefix: str,
):
"""Checks that the output of `network.create_variables` matches a spec.
Args:
network_output_spec: The output of `network.create_variables`.
spec: The spec we are ... | Checks that the output of `network.create_variables` matches a spec.
Args:
network_output_spec: The output of `network.create_variables`.
spec: The spec we are matching to.
message_prefix: The message prefix for error messages, used when the specs
don't match.
Raises:
ValueError: If the spec... | assert_specs_are_compatible | python | tensorflow/agents | tf_agents/distributions/utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/utils.py | Apache-2.0 |
def __init__(
self,
env,
policy,
observers=None,
transition_observers=None,
info_observers=None,
):
"""Creates a Driver.
Args:
env: An environment.Base environment.
policy: A policy.Base policy.
observers: A list of observers that are updated after the dr... | Creates a Driver.
Args:
env: An environment.Base environment.
policy: A policy.Base policy.
observers: A list of observers that are updated after the driver is run.
Each observer is a callable(Trajectory) that returns the input.
Trajectory.time_step is a stacked batch [N+1, batch_... | __init__ | python | tensorflow/agents | tf_agents/drivers/driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/driver.py | Apache-2.0 |
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