code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
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 __call__(self, observation, actions=None):
"""Returns the probability of input actions being feasible."""
batch_size = tf.shape(observation)[0]
num_actions = self._action_spec.maximum - self._action_spec.minimum + 1
feasibility_prob = 0.5 * tf.ones([batch_size, num_actions], tf.float32)
return f... | Returns the probability of input actions being feasible. | __call__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints_test.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)
feasibility_prob = tf.cast(tf.greater(actions, 2), tf.float32)
return feasibility_prob | Returns the probability of input actions being feasible. | __call__ | python | tensorflow/agents | tf_agents/bandits/policies/constraints_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/constraints_test.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 __init__(
self,
observation_spec: types.NestedTensorSpec,
kernel_weights: np.ndarray,
bias: np.ndarray,
):
"""A simple linear network.
Args:
observation_spec: The observation specification.
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bia... | A simple linear network.
Args:
observation_spec: The observation specification.
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
| __init__ | python | tensorflow/agents | tf_agents/bandits/policies/greedy_multi_objective_neural_policy_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/greedy_multi_objective_neural_policy_test.py | Apache-2.0 |
def __init__(self, kernel_weights: np.ndarray, bias: np.ndarray):
"""A simple linear heteroscedastic network.
Args:
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
"""
assert len(kernel_weights.shape) == 2
assert len(... | A simple linear heteroscedastic network.
Args:
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
| __init__ | python | tensorflow/agents | tf_agents/bandits/policies/greedy_multi_objective_neural_policy_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/policies/greedy_multi_objective_neural_policy_test.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 _get_predicted_rewards_from_per_arm_linucb(
self, observation_numpy, batch_size
):
"""Runs one step of LinUCB using numpy arrays."""
observation_numpy.reshape(
[batch_size, self._num_actions, self._encoding_dim]
)
predicted_rewards = []
for k in range(self._num_actions):
a... | Runs one step of LinUCB using numpy arrays. | _get_predicted_rewards_from_per_arm_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 testCopy(self):
"""Confirm we can copy the distribution."""
distribution = shifted_categorical.ShiftedCategorical(
logits=[100.0, 100.0, 100.0], shift=2
)
copy = distribution.copy()
with self.cached_session() as s:
probs_np = s.run(copy.probs_parameter())
logits_np = s.run(co... | Confirm we can copy the distribution. | testCopy | python | tensorflow/agents | tf_agents/distributions/shifted_categorical_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/distributions/shifted_categorical_test.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 |
def __init__(
self,
env,
policy,
observers=None,
transition_observers=None,
num_episodes=1,
):
"""Creates a DynamicEpisodeDriver.
**Note** about bias when using batched environments with `num_episodes`:
When using `num_episodes != None`, a `run` step "finishes" when
... | Creates a DynamicEpisodeDriver.
**Note** about bias when using batched environments with `num_episodes`:
When using `num_episodes != None`, a `run` step "finishes" when
`num_episodes` have been completely collected (hit a boundary).
When used in conjunction with environments that have variable-length
... | __init__ | python | tensorflow/agents | tf_agents/drivers/dynamic_episode_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_episode_driver.py | Apache-2.0 |
def _loop_condition_fn(self, num_episodes):
"""Returns a function with the condition needed for tf.while_loop."""
def loop_cond(counter, *_):
"""Determines when to stop the loop, based on episode counter.
Args:
counter: Episode counters per batch index. Shape [batch_size] when
ba... | Returns a function with the condition needed for tf.while_loop. | _loop_condition_fn | python | tensorflow/agents | tf_agents/drivers/dynamic_episode_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_episode_driver.py | Apache-2.0 |
def _loop_body_fn(self):
"""Returns a function with the driver's loop body ops."""
def loop_body(counter, time_step, policy_state):
"""Runs a step in environment.
While loop will call multiple times.
Args:
counter: Episode counters per batch index. Shape [batch_size].
time_s... | Returns a function with the driver's loop body ops. | _loop_body_fn | python | tensorflow/agents | tf_agents/drivers/dynamic_episode_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_episode_driver.py | Apache-2.0 |
def loop_body(counter, time_step, policy_state):
"""Runs a step in environment.
While loop will call multiple times.
Args:
counter: Episode counters per batch index. Shape [batch_size].
time_step: TimeStep tuple with elements shape [batch_size, ...].
policy_state: Poicy state... | Runs a step in environment.
While loop will call multiple times.
Args:
counter: Episode counters per batch index. Shape [batch_size].
time_step: TimeStep tuple with elements shape [batch_size, ...].
policy_state: Poicy state tensor shape [batch_size, policy_state_dim].
Pa... | loop_body | python | tensorflow/agents | tf_agents/drivers/dynamic_episode_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_episode_driver.py | Apache-2.0 |
def run(
self,
time_step=None,
policy_state=None,
num_episodes=None,
maximum_iterations=None,
):
"""Takes episodes in the environment using the policy and update observers.
If `time_step` and `policy_state` are not provided, `run` will reset the
environment and request an in... | Takes episodes in the environment using the policy and update observers.
If `time_step` and `policy_state` are not provided, `run` will reset the
environment and request an initial state from the policy.
**Note** about bias when using batched environments with `num_episodes`:
When using `num_episodes ... | run | python | tensorflow/agents | tf_agents/drivers/dynamic_episode_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_episode_driver.py | Apache-2.0 |
def __init__(
self,
env,
policy,
observers=None,
transition_observers=None,
num_steps=1,
):
"""Creates a DynamicStepDriver.
Args:
env: A tf_environment.Base environment.
policy: A tf_policy.TFPolicy policy.
observers: A list of observers that are updated ... | Creates a DynamicStepDriver.
Args:
env: A tf_environment.Base environment.
policy: A tf_policy.TFPolicy policy.
observers: A list of observers that are updated after every step in the
environment. Each observer is a callable(time_step.Trajectory).
transition_observers: A list of obs... | __init__ | python | tensorflow/agents | tf_agents/drivers/dynamic_step_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_step_driver.py | Apache-2.0 |
def _loop_condition_fn(self):
"""Returns a function with the condition needed for tf.while_loop."""
def loop_cond(counter, *_):
"""Determines when to stop the loop, based on step counter.
Args:
counter: Step counters per batch index. Shape [batch_size] when
batch_size > 1, else s... | Returns a function with the condition needed for tf.while_loop. | _loop_condition_fn | python | tensorflow/agents | tf_agents/drivers/dynamic_step_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_step_driver.py | Apache-2.0 |
def _loop_body_fn(self):
"""Returns a function with the driver's loop body ops."""
def loop_body(counter, time_step, policy_state):
"""Runs a step in environment.
While loop will call multiple times.
Args:
counter: Step counters per batch index. Shape [batch_size].
time_step... | Returns a function with the driver's loop body ops. | _loop_body_fn | python | tensorflow/agents | tf_agents/drivers/dynamic_step_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_step_driver.py | Apache-2.0 |
def loop_body(counter, time_step, policy_state):
"""Runs a step in environment.
While loop will call multiple times.
Args:
counter: Step counters per batch index. Shape [batch_size].
time_step: TimeStep tuple with elements shape [batch_size, ...].
policy_state: Policy state t... | Runs a step in environment.
While loop will call multiple times.
Args:
counter: Step counters per batch index. Shape [batch_size].
time_step: TimeStep tuple with elements shape [batch_size, ...].
policy_state: Policy state tensor shape [batch_size, policy_state_dim].
Pass... | loop_body | python | tensorflow/agents | tf_agents/drivers/dynamic_step_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_step_driver.py | Apache-2.0 |
def run(self, time_step=None, policy_state=None, maximum_iterations=None):
"""Takes steps in the environment using the policy while updating observers.
Args:
time_step: optional initial time_step. If None, it will use the
current_time_step of the environment. Elements should be shape
[bat... | Takes steps in the environment using the policy while updating observers.
Args:
time_step: optional initial time_step. If None, it will use the
current_time_step of the environment. Elements should be shape
[batch_size, ...].
policy_state: optional initial state for the policy.
ma... | run | python | tensorflow/agents | tf_agents/drivers/dynamic_step_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/dynamic_step_driver.py | Apache-2.0 |
def __init__(
self,
env: py_environment.PyEnvironment,
policy: py_policy.PyPolicy,
observers: Sequence[Callable[[trajectory.Trajectory], Any]],
transition_observers: Optional[
Sequence[Callable[[trajectory.Transition], Any]]
] = None,
info_observers: Optional[Sequence... | A driver that runs a python policy in a python environment.
**Note** about bias when using batched environments with `max_episodes`:
When using `max_episodes != None`, a `run` step "finishes" when
`max_episodes` have been completely collected (hit a boundary).
When used in conjunction with environments... | __init__ | python | tensorflow/agents | tf_agents/drivers/py_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/py_driver.py | Apache-2.0 |
def run( # pytype: disable=signature-mismatch # overriding-parameter-count-checks
self, time_step: ts.TimeStep, policy_state: types.NestedArray = ()
) -> Tuple[ts.TimeStep, types.NestedArray]:
"""Run policy in environment given initial time_step and policy_state.
Args:
time_step: The initial ti... | Run policy in environment given initial time_step and policy_state.
Args:
time_step: The initial time_step.
policy_state: The initial policy_state.
Returns:
A tuple (final time_step, final policy_state).
| run | python | tensorflow/agents | tf_agents/drivers/py_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/py_driver.py | Apache-2.0 |
def make_random_trajectory():
"""Creates a random trajectory.
This trajectory contains Tensors shaped `[1, 6, ...]` where `1` is the batch
and `6` is the number of time steps.
Observations are unbounded but actions are bounded to take values within
`[1, 2]`.
Policy info is also provided, and is equal to ... | Creates a random trajectory.
This trajectory contains Tensors shaped `[1, 6, ...]` where `1` is the batch
and `6` is the number of time steps.
Observations are unbounded but actions are bounded to take values within
`[1, 2]`.
Policy info is also provided, and is equal to the actions. It can be removed
v... | make_random_trajectory | python | tensorflow/agents | tf_agents/drivers/test_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/test_utils.py | Apache-2.0 |
def __init__(
self,
env: tf_environment.TFEnvironment,
policy: tf_policy.TFPolicy,
observers: Sequence[Callable[[trajectory.Trajectory], Any]],
transition_observers: Optional[
Sequence[Callable[[trajectory.Transition], Any]]
] = None,
max_steps: Optional[types.Int] = ... | A driver that runs a TF policy in a TF environment.
**Note** about bias when using batched environments with `max_episodes`:
When using `max_episodes != None`, a `run` step "finishes" when
`max_episodes` have been completely collected (hit a boundary).
When used in conjunction with environments that ha... | __init__ | python | tensorflow/agents | tf_agents/drivers/tf_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/tf_driver.py | Apache-2.0 |
def run( # pytype: disable=signature-mismatch # overriding-parameter-count-checks
self, time_step: ts.TimeStep, policy_state: types.NestedTensor = ()
) -> Tuple[ts.TimeStep, types.NestedTensor]:
"""Run policy in environment given initial time_step and policy_state.
Args:
time_step: The initial ... | Run policy in environment given initial time_step and policy_state.
Args:
time_step: The initial time_step.
policy_state: The initial policy_state.
Returns:
A tuple (final time_step, final policy_state).
| run | python | tensorflow/agents | tf_agents/drivers/tf_driver.py | https://github.com/tensorflow/agents/blob/master/tf_agents/drivers/tf_driver.py | Apache-2.0 |
def __init__(
self,
env: gym.Env,
frame_skip: int = 4,
terminal_on_life_loss: bool = False,
screen_size: int = 84,
):
"""Constructor for an Atari 2600 preprocessor.
Args:
env: Gym environment whose observations are preprocessed.
frame_skip: int, the frequency at whic... | Constructor for an Atari 2600 preprocessor.
Args:
env: Gym environment whose observations are preprocessed.
frame_skip: int, the frequency at which the agent experiences the game.
terminal_on_life_loss: bool, If True, the step() method returns
is_terminal=True whenever a life is lost. See... | __init__ | python | tensorflow/agents | tf_agents/environments/atari_preprocessing.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/atari_preprocessing.py | Apache-2.0 |
def step(self, action: np.ndarray) -> np.ndarray:
"""Applies the given action in the environment.
Remarks:
* If a terminal state (from life loss or episode end) is reached, this may
execute fewer than self.frame_skip steps in the environment.
* Furthermore, in this case the returned observ... | Applies the given action in the environment.
Remarks:
* If a terminal state (from life loss or episode end) is reached, this may
execute fewer than self.frame_skip steps in the environment.
* Furthermore, in this case the returned observation may not contain valid
image data and should... | step | python | tensorflow/agents | tf_agents/environments/atari_preprocessing.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/atari_preprocessing.py | Apache-2.0 |
def _pool_and_resize(self):
"""Transforms two frames into a Nature DQN observation.
For efficiency, the transformation is done in-place in self.screen_buffer.
Returns:
transformed_screen: numpy array, pooled, resized screen.
"""
# Pool if there are enough screens to do so.
if self.frame_... | Transforms two frames into a Nature DQN observation.
For efficiency, the transformation is done in-place in self.screen_buffer.
Returns:
transformed_screen: numpy array, pooled, resized screen.
| _pool_and_resize | python | tensorflow/agents | tf_agents/environments/atari_preprocessing.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/atari_preprocessing.py | Apache-2.0 |
def __init__(
self,
envs: Sequence[py_environment.PyEnvironment],
multithreading: bool = True,
):
"""Batch together multiple (non-batched) py environments.
The environments can be different but must use the same action and
observation specs.
Args:
envs: List python environmen... | Batch together multiple (non-batched) py environments.
The environments can be different but must use the same action and
observation specs.
Args:
envs: List python environments (must be non-batched).
multithreading: Python bool describing whether interactions with the given
environmen... | __init__ | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def _reset(self):
"""Reset all environments and combine the resulting observation.
Returns:
Time step with batch dimension.
"""
if self._num_envs == 1:
return nest_utils.batch_nested_array(self._envs[0].reset())
else:
time_steps = self._execute(lambda env: env.reset(), self._envs)... | Reset all environments and combine the resulting observation.
Returns:
Time step with batch dimension.
| _reset | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def _step(self, actions):
"""Forward a batch of actions to the wrapped environments.
Args:
actions: Batched action, possibly nested, to apply to the environment.
Raises:
ValueError: Invalid actions.
Returns:
Batch of observations, rewards, and done flags.
"""
if self._num_e... | Forward a batch of actions to the wrapped environments.
Args:
actions: Batched action, possibly nested, to apply to the environment.
Raises:
ValueError: Invalid actions.
Returns:
Batch of observations, rewards, and done flags.
| _step | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def set_state(self, state: Sequence[Any]) -> None:
"""Restores the environment to a given `state`."""
self._execute(
lambda env_state: env_state[0].set_state(env_state[1]),
zip(self._envs, state)
) | Restores the environment to a given `state`. | set_state | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def close(self) -> None:
"""Send close messages to the external process and join them."""
self._execute(lambda env: env.close(), self._envs)
if self._parallel_execution:
self._pool.close()
self._pool.join() | Send close messages to the external process and join them. | close | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def unstack_actions(batched_actions: types.NestedArray) -> types.NestedArray:
"""Returns a list of actions from potentially nested batch of actions."""
flattened_actions = tf.nest.flatten(batched_actions)
unstacked_actions = [
tf.nest.pack_sequence_as(batched_actions, actions)
for actions in zip(*flat... | Returns a list of actions from potentially nested batch of actions. | unstack_actions | python | tensorflow/agents | tf_agents/environments/batched_py_environment.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/batched_py_environment.py | Apache-2.0 |
def convert_time_step(time_step):
"""Convert to agents time_step type as the __hash__ method is different."""
reward = time_step.reward
if reward is None:
reward = 0.0
discount = time_step.discount
if discount is None:
discount = 1.0
observation = tf.nest.map_structure(_maybe_float32, time_step.obs... | Convert to agents time_step type as the __hash__ method is different. | convert_time_step | python | tensorflow/agents | tf_agents/environments/dm_control_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/dm_control_wrapper.py | Apache-2.0 |
def spec_from_gym_space(
space: gym.Space,
simplify_box_bounds: bool = True,
name: Optional[Text] = None,
) -> Union[
specs.BoundedArraySpec,
specs.ArraySpec,
tuple[specs.ArraySpec, ...],
list[specs.ArraySpec],
collections.OrderedDict[str, specs.ArraySpec],
]:
"""Converts gymnasium spa... | Converts gymnasium spaces into array specs, or a collection thereof.
Please note:
Unlike OpenAI's gym, Farama's gymnasium provides a dtype for
each current implementation of spaces. dtype should be defined
in all specific subclasses of gymnasium.Space even if it is still
optional in the superclass.
... | spec_from_gym_space | python | tensorflow/agents | tf_agents/environments/gymnasium_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/gymnasium_wrapper.py | Apache-2.0 |
def try_simplify_array_to_value(np_array):
"""If given numpy array has all the same values, returns that value."""
first_value = np_array.item(0)
if np.all(np_array == first_value):
return np.array(first_value, dtype=np_array.dtype)
else:
return np_array | If given numpy array has all the same values, returns that value. | try_simplify_array_to_value | python | tensorflow/agents | tf_agents/environments/gymnasium_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/gymnasium_wrapper.py | Apache-2.0 |
def nested_spec(spec, child_name):
"""Returns the nested spec with a unique name."""
nested_name = name + '/' + child_name if name else child_name
return spec_from_gym_space(spec, simplify_box_bounds, nested_name) | Returns the nested spec with a unique name. | nested_spec | python | tensorflow/agents | tf_agents/environments/gymnasium_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/gymnasium_wrapper.py | Apache-2.0 |
def __getattr__(self, name: Text) -> Any:
"""Forward all other calls to the base environment."""
gym_env = super(GymnasiumWrapper, self).__getattribute__('_gym_env')
return getattr(gym_env, name) | Forward all other calls to the base environment. | __getattr__ | python | tensorflow/agents | tf_agents/environments/gymnasium_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/gymnasium_wrapper.py | Apache-2.0 |
def spec_from_gym_space(
space: gym.Space,
dtype_map: Optional[Dict[gym.Space, np.dtype]] = None,
simplify_box_bounds: bool = True,
name: Optional[Text] = None,
) -> specs.BoundedArraySpec:
"""Converts gym spaces into array specs.
Gym does not properly define dtypes for spaces. By default all space... | Converts gym spaces into array specs.
Gym does not properly define dtypes for spaces. By default all spaces set
their type to float64 even though observations do not always return this type.
See:
https://github.com/openai/gym/issues/527
To handle this we allow a dtype_map for setting default types for mappi... | spec_from_gym_space | python | tensorflow/agents | tf_agents/environments/gym_wrapper.py | https://github.com/tensorflow/agents/blob/master/tf_agents/environments/gym_wrapper.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.