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
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|---|---|---|---|---|---|---|---|
def __getitem__(self, key):
"""See :meth:`object.__getitem__`.
Args:
key (Hashable): Key associated with the value to retrieve.
Returns:
object: Lazily-evaluated value of the :class:`Callable` associated
with key.
"""
if key not in self._d... | See :meth:`object.__getitem__`.
Args:
key (Hashable): Key associated with the value to retrieve.
Returns:
object: Lazily-evaluated value of the :class:`Callable` associated
with key.
| __getitem__ | python | rlworkgroup/garage | src/garage/tf/optimizers/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/optimizers/_dtypes.py | MIT |
def get(self, key, default=None):
"""See :meth:`dict.get`.
Args:
key (Hashable): Key associated with the value to retreive.
default (object): Value to return if key is not present in this
:class:`LazyDict`.
Returns:
object: Value associated wi... | See :meth:`dict.get`.
Args:
key (Hashable): Key associated with the value to retreive.
default (object): Value to return if key is not present in this
:class:`LazyDict`.
Returns:
object: Value associated with key if the key is present, otherwise
... | get | python | rlworkgroup/garage | src/garage/tf/optimizers/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/optimizers/_dtypes.py | MIT |
def update_plot(self, policy, max_length=np.inf):
"""Update the policy being plotted.
Args:
policy (garage.tf.Policy): Policy to visualize.
max_length (int or float): The maximum length to allow an episode
to be. Defaults to infinity.
"""
if self... | Update the policy being plotted.
Args:
policy (garage.tf.Policy): Policy to visualize.
max_length (int or float): The maximum length to allow an episode
to be. Defaults to infinity.
| update_plot | python | rlworkgroup/garage | src/garage/tf/plotter/plotter.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/plotter/plotter.py | MIT |
def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
"""
sample, prob = self.get_actions([o... | Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_cnn_policy.py | MIT |
def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
"""
if not isinstance(obse... | Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_cnn_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_prob']
return new_dict | Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_cnn_policy.py | MIT |
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.OneHotCategorical: Policy distribution.
tf.Tensor: Ste... | Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.OneHotCategorical: Policy distribution.
tf.Tensor: Step output, with shape :math:`(N, S^*)`.
t... | build | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def reset(self, do_resets=None):
"""Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
... | Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
do_resets (numpy.ndarray): Bool that indi... | reset | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
"""
actions, agent_infos = self.get_ac... | Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
"""
if not isinstance(obse... | Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def state_info_specs(self):
"""State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
"""
if self._state_include_action:
return [
('prev_action', (self... | State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
| state_info_specs | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_step_prob']
del new_dict['_init_hidden']
return new_dict | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_gru_policy.py | MIT |
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.OneHotCategorical: Policy distribution.
tf.Tensor: Ste... | Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.OneHotCategorical: Policy distribution.
tf.Tensor: Step output, with shape :math:`(N, S^*)`
tf... | build | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def reset(self, do_resets=None):
"""Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
... | Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
do_resets (numpy.ndarray): Bool that indi... | reset | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
"""
actions, agent_infos = self.get_ac... | Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
"""
if not isinstance(obse... | Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def state_info_specs(self):
"""State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
"""
if self._state_include_action:
return [
('prev_action', (self... | State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
| state_info_specs | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_step_prob']
del new_dict['_init_hidden']
del new_dict['_init_cell']
return new_dic... | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_lstm_policy.py | MIT |
def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
"""
actions, agent_infos = self.get_ac... | Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/categorical_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_mlp_policy.py | MIT |
def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
"""
if not isinstance(obse... | Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/categorical_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_mlp_policy.py | MIT |
def get_regularizable_vars(self):
"""Get regularizable weight variables under the Policy scope.
Returns:
list[tf.Tensor]: Trainable variables.
"""
trainable = self.get_trainable_vars()
return [
var for var in trainable
if 'hidden' in var.name... | Get regularizable weight variables under the Policy scope.
Returns:
list[tf.Tensor]: Trainable variables.
| get_regularizable_vars | python | rlworkgroup/garage | src/garage/tf/policies/categorical_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/categorical_mlp_policy.py | MIT |
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Predicted action.
dict: Empty dict since this policy does not model a... | Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Predicted action.
dict: Empty dict since this policy does not model a distribution.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/continuous_mlp_policy.py | MIT |
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Predicted actions.
dict: Empty dict since this policy does no... | Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Predicted actions.
dict: Empty dict since this policy does not model a distribution.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/continuous_mlp_policy.py | MIT |
def get_regularizable_vars(self):
"""Get regularizable weight variables under the Policy scope.
Returns:
list(tf.Variable): List of regularizable variables.
"""
trainable = self.get_trainable_vars()
return [
var for var in trainable
if 'hidde... | Get regularizable weight variables under the Policy scope.
Returns:
list(tf.Variable): List of regularizable variables.
| get_regularizable_vars | python | rlworkgroup/garage | src/garage/tf/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/continuous_mlp_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled as the contents for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_prob']
return new_dict | Object.__getstate__.
Returns:
dict: the state to be pickled as the contents for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/continuous_mlp_policy.py | MIT |
def get_action(self, observation):
"""Get action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Single optimal action from this policy.
dict: Predicted action and agent inf... | Get action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Single optimal action from this policy.
dict: Predicted action and agent information. It returns an empty
... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/discrete_qf_argmax_policy.py | MIT |
def get_actions(self, observations):
"""Get actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Optimal actions from this policy.
dict: Predicted action and agent inf... | Get actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Optimal actions from this policy.
dict: Predicted action and agent information. It returns an empty
di... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/discrete_qf_argmax_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_qval']
return new_dict | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/discrete_qf_argmax_policy.py | MIT |
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Policy distribution.
tf.Tensor... | Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Policy distribution.
tf.Tensor: Step means, with shape :math:`(N, S^*)`.
... | build | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def reset(self, do_resets=None):
"""Reset the policy.
Note:
If `do_resets` is None, it will be by default `np.array([True])`
which implies the policy will not be "vectorized", i.e. number of
parallel environments for training data sampling = 1.
Args:
... | Reset the policy.
Note:
If `do_resets` is None, it will be by default `np.array([True])`
which implies the policy will not be "vectorized", i.e. number of
parallel environments for training data sampling = 1.
Args:
do_resets (numpy.ndarray): Bool that in... | reset | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
... | Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a dict, w... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
... | Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a d... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def state_info_specs(self):
"""State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
"""
if self._state_include_action:
return [
('prev_action', (self... | State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
| state_info_specs | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_step_mean_std']
del new_dict['_init_hidden']
return new_dict | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_gru_policy.py | MIT |
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Policy distribution.
tf.Tensor... | Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Policy distribution.
tf.Tensor: Step means, with shape :math:`(N, S^*)`.
... | build | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def reset(self, do_resets=None):
"""Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
... | Reset the policy.
Note:
If `do_resets` is None, it will be by default np.array([True]),
which implies the policy will not be "vectorized", i.e. number of
paralle environments for training data sampling = 1.
Args:
do_resets (numpy.ndarray): Bool that indi... | reset | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
... | Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a dict, w... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
... | Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a d... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def state_info_specs(self):
"""State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
"""
if self._state_include_action:
return [
('prev_action', (self... | State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
| state_info_specs | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_step_mean_std']
del new_dict['_init_hidden']
del new_dict['_init_cell']
return new... | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_lstm_policy.py | MIT |
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
... | Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a dict, w... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_policy.py | MIT |
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
... | Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns actions and a dic... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_dist']
return new_dict | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_policy.py | MIT |
def _initialize(self):
"""Build policy to support sampling.
After build, get_action_*() methods will be available.
"""
obs_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, None, self.obs_dim))
encoder_input = tf.compat.v1.pl... | Build policy to support sampling.
After build, get_action_*() methods will be available.
| _initialize | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def build(self, obs_input, task_input, name=None):
"""Build policy.
Args:
obs_input (tf.Tensor): Observation input.
task_input (tf.Tensor): One-hot task id input.
name (str): Name of the model, which is also the name scope.
Returns:
namedtuple: P... | Build policy.
Args:
obs_input (tf.Tensor): Observation input.
task_input (tf.Tensor): One-hot task id input.
name (str): Name of the model, which is also the name scope.
Returns:
namedtuple: Policy network.
namedtuple: Encoder network.
... | build | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_action(self, observation):
"""Get action sampled from the policy.
Args:
observation (np.ndarray): Augmented observation from the
environment, with shape :math:`(O+N, )`. O is the dimension
of observation, N is the number of tasks.
Returns:
... | Get action sampled from the policy.
Args:
observation (np.ndarray): Augmented observation from the
environment, with shape :math:`(O+N, )`. O is the dimension
of observation, N is the number of tasks.
Returns:
np.ndarray: Action sampled from the ... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_actions(self, observations):
"""Get actions sampled from the policy.
Args:
observations (np.ndarray): Augmented observation from the
environment, with shape :math:`(T, O+N)`. T is the number of
environment steps, O is the dimension of observation, N i... | Get actions sampled from the policy.
Args:
observations (np.ndarray): Augmented observation from the
environment, with shape :math:`(T, O+N)`. T is the number of
environment steps, O is the dimension of observation, N is the
number of tasks.
... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_action_given_latent(self, observation, latent):
"""Sample an action given observation and latent.
Args:
observation (np.ndarray): Observation from the environment,
with shape :math:`(O, )`. O is the dimension of observation.
latent (np.ndarray): Latent, w... | Sample an action given observation and latent.
Args:
observation (np.ndarray): Observation from the environment,
with shape :math:`(O, )`. O is the dimension of observation.
latent (np.ndarray): Latent, with shape :math:`(Z, )`. Z is the
dimension of the ... | get_action_given_latent | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_actions_given_latents(self, observations, latents):
"""Sample a batch of actions given observations and latents.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps, O
is ... | Sample a batch of actions given observations and latents.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps, O
is the dimension of observation.
latents (np.ndarray): Latents... | get_actions_given_latents | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_action_given_task(self, observation, task_id):
"""Sample an action given observation and task id.
Args:
observation (np.ndarray): Observation from the environment, with
shape :math:`(O, )`. O is the dimension of the observation.
task_id (np.ndarray): One-... | Sample an action given observation and task id.
Args:
observation (np.ndarray): Observation from the environment, with
shape :math:`(O, )`. O is the dimension of the observation.
task_id (np.ndarray): One-hot task id, with shape :math:`(N, ).
N is the num... | get_action_given_task | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_actions_given_tasks(self, observations, task_ids):
"""Sample a batch of actions given observations and task ids.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps,
O is ... | Sample a batch of actions given observations and task ids.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps,
O is the dimension of observation.
task_ids (np.ndarry): One-ho... | get_actions_given_tasks | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_dist_obs_latent']
del new_dict['_f_dist_obs_task']
return new_dict | Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/gaussian_mlp_task_embedding_policy.py | MIT |
def get_action(self, observation):
"""Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Action and extra agent
info.
""" | Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Action and extra agent
info.
| get_action | python | rlworkgroup/garage | src/garage/tf/policies/policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/policy.py | MIT |
def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
""" | Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
| get_actions | python | rlworkgroup/garage | src/garage/tf/policies/policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/policy.py | MIT |
def get_action(self, observation):
"""Get action sampled from the policy.
Args:
observation (np.ndarray): Augmented observation from the
environment, with shape :math:`(O+N, )`. O is the dimension of
observation, N is the number of tasks.
Returns:
... | Get action sampled from the policy.
Args:
observation (np.ndarray): Augmented observation from the
environment, with shape :math:`(O+N, )`. O is the dimension of
observation, N is the number of tasks.
Returns:
np.ndarray: Action sampled from the ... | get_action | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_actions(self, observations):
"""Get actions sampled from the policy.
Args:
observations (np.ndarray): Augmented observation from the
environment, with shape :math:`(T, O+N)`. T is the number of
environment steps, O is the dimension of observation, N i... | Get actions sampled from the policy.
Args:
observations (np.ndarray): Augmented observation from the
environment, with shape :math:`(T, O+N)`. T is the number of
environment steps, O is the dimension of observation, N is the
number of tasks.
... | get_actions | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_action_given_task(self, observation, task_id):
"""Sample an action given observation and task id.
Args:
observation (np.ndarray): Observation from the environment, with
shape :math:`(O, )`. O is the dimension of the observation.
task_id (np.ndarray): One-... | Sample an action given observation and task id.
Args:
observation (np.ndarray): Observation from the environment, with
shape :math:`(O, )`. O is the dimension of the observation.
task_id (np.ndarray): One-hot task id, with shape :math:`(N, ).
N is the num... | get_action_given_task | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_actions_given_tasks(self, observations, task_ids):
"""Sample a batch of actions given observations and task ids.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps,
O is ... | Sample a batch of actions given observations and task ids.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps,
O is the dimension of observation.
task_ids (np.ndarry): One-ho... | get_actions_given_tasks | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_action_given_latent(self, observation, latent):
"""Sample an action given observation and latent.
Args:
observation (np.ndarray): Observation from the environment,
with shape :math:`(O, )`. O is the dimension of observation.
latent (np.ndarray): Latent, w... | Sample an action given observation and latent.
Args:
observation (np.ndarray): Observation from the environment,
with shape :math:`(O, )`. O is the dimension of observation.
latent (np.ndarray): Latent, with shape :math:`(Z, )`. Z is the
dimension of late... | get_action_given_latent | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_actions_given_latents(self, observations, latents):
"""Sample a batch of actions given observations and latents.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps, O
is ... | Sample a batch of actions given observations and latents.
Args:
observations (np.ndarray): Observations from the environment, with
shape :math:`(T, O)`. T is the number of environment steps, O
is the dimension of observation.
latents (np.ndarray): Latents... | get_actions_given_latents | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def split_augmented_observation(self, collated):
"""Splits up observation into one-hot task and environment observation.
Args:
collated (np.ndarray): Environment observation concatenated with
task one-hot, with shape :math:`(O+N, )`. O is the dimension of
obs... | Splits up observation into one-hot task and environment observation.
Args:
collated (np.ndarray): Environment observation concatenated with
task one-hot, with shape :math:`(O+N, )`. O is the dimension of
observation, N is the number of tasks.
Returns:
... | split_augmented_observation | python | rlworkgroup/garage | src/garage/tf/policies/task_embedding_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/policies/task_embedding_policy.py | MIT |
def get_qval(self, observation, action):
"""Q Value of the network.
Args:
observation (np.ndarray): Observation input of shape
:math:`(N, O*)`.
action (np.ndarray): Action input of shape :math:`(N, A*)`.
Returns:
np.ndarray: Array of shape :m... | Q Value of the network.
Args:
observation (np.ndarray): Observation input of shape
:math:`(N, O*)`.
action (np.ndarray): Action input of shape :math:`(N, A*)`.
Returns:
np.ndarray: Array of shape :math:`(N, )` containing Q values
corr... | get_qval | python | rlworkgroup/garage | src/garage/tf/q_functions/continuous_cnn_q_function.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/q_functions/continuous_cnn_q_function.py | MIT |
def build(self, state_input, action_input, name):
"""Build the symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor of shape
:math:`(N, O*)`.
action_input (tf.Tensor): The action input tf.Tensor of shape
:math:`(N... | Build the symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor of shape
:math:`(N, O*)`.
action_input (tf.Tensor): The action input tf.Tensor of shape
:math:`(N, A*)`.
name (str): Network variable scope.
... | build | python | rlworkgroup/garage | src/garage/tf/q_functions/continuous_cnn_q_function.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/q_functions/continuous_cnn_q_function.py | MIT |
def build(self, state_input, name):
"""Build the symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor to the network.
name (str): Network variable scope.
Return:
tf.Tensor: The tf.Tensor output of Discrete CNN QFunction.
... | Build the symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor to the network.
name (str): Network variable scope.
Return:
tf.Tensor: The tf.Tensor output of Discrete CNN QFunction.
| build | python | rlworkgroup/garage | src/garage/tf/q_functions/discrete_cnn_q_function.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/q_functions/discrete_cnn_q_function.py | MIT |
def __call__(self, *args, **kwargs):
"""Construct the inner class and wrap it.
Args:
*args: Passed on to inner worker class.
**kwargs: Passed on to inner worker class.
Returns:
TFWorkerWrapper: The wrapped worker.
"""
wrapper = TFWorkerWrapp... | Construct the inner class and wrap it.
Args:
*args: Passed on to inner worker class.
**kwargs: Passed on to inner worker class.
Returns:
TFWorkerWrapper: The wrapped worker.
| __call__ | python | rlworkgroup/garage | src/garage/tf/samplers/worker.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/samplers/worker.py | MIT |
def zero_optim_grads(optim, set_to_none=True):
"""Sets the gradient of all optimized tensors to None.
This is an optimization alternative to calling `optimizer.zero_grad()`
Args:
optim (torch.nn.Optimizer): The optimizer instance
to zero parameter gradients.
set_to_none (bool):... | Sets the gradient of all optimized tensors to None.
This is an optimization alternative to calling `optimizer.zero_grad()`
Args:
optim (torch.nn.Optimizer): The optimizer instance
to zero parameter gradients.
set_to_none (bool): Set gradients to None
instead of calling ... | zero_optim_grads | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def compute_advantages(discount, gae_lambda, max_episode_length, baselines,
rewards):
"""Calculate advantages.
Advantages are a discounted cumulative sum.
Calculate advantages using a baseline according to Generalized Advantage
Estimation (GAE)
The discounted cumulative sum... | Calculate advantages.
Advantages are a discounted cumulative sum.
Calculate advantages using a baseline according to Generalized Advantage
Estimation (GAE)
The discounted cumulative sum can be computed using conv2d with filter.
filter:
[1, (discount * gae_lambda), (discount * gae_lambda) ... | compute_advantages | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def pad_to_last(nums, total_length, axis=-1, val=0):
"""Pad val to last in nums in given axis.
length of the result in given axis should be total_length.
Raises:
IndexError: If the input axis value is out of range of the nums array
Args:
nums (numpy.ndarray): The array to pad.
t... | Pad val to last in nums in given axis.
length of the result in given axis should be total_length.
Raises:
IndexError: If the input axis value is out of range of the nums array
Args:
nums (numpy.ndarray): The array to pad.
total_length (int): The final width of the Array.
axi... | pad_to_last | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def np_to_torch(array):
"""Numpy arrays to PyTorch tensors.
Args:
array (np.ndarray): Data in numpy array.
Returns:
torch.Tensor: float tensor on the global device.
"""
tensor = torch.from_numpy(array)
if tensor.dtype != torch.float32:
tensor = tensor.float()
ret... | Numpy arrays to PyTorch tensors.
Args:
array (np.ndarray): Data in numpy array.
Returns:
torch.Tensor: float tensor on the global device.
| np_to_torch | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def as_torch_dict(array_dict):
"""Convert a dict whose values are numpy arrays to PyTorch tensors.
Modifies array_dict in place.
Args:
array_dict (dict): Dictionary of data in numpy arrays
Returns:
dict: Dictionary of data in PyTorch tensors
"""
for key, value in array_dict.i... | Convert a dict whose values are numpy arrays to PyTorch tensors.
Modifies array_dict in place.
Args:
array_dict (dict): Dictionary of data in numpy arrays
Returns:
dict: Dictionary of data in PyTorch tensors
| as_torch_dict | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def flatten_to_single_vector(tensor):
"""Collapse the C x H x W values per representation into a single long vector.
Reshape a tensor of size (N, C, H, W) into (N, C * H * W).
Args:
tensor (torch.tensor): batch of data.
Returns:
torch.Tensor: Reshaped view of that data (analogous to n... | Collapse the C x H x W values per representation into a single long vector.
Reshape a tensor of size (N, C, H, W) into (N, C * H * W).
Args:
tensor (torch.tensor): batch of data.
Returns:
torch.Tensor: Reshaped view of that data (analogous to numpy.reshape)
| flatten_to_single_vector | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def update_module_params(module, new_params): # noqa: D202
"""Load parameters to a module.
This function acts like `torch.nn.Module._load_from_state_dict()`, but
it replaces the tensors in module with those in new_params, while
`_load_from_state_dict()` loads only the value. Use this function so
t... | Load parameters to a module.
This function acts like `torch.nn.Module._load_from_state_dict()`, but
it replaces the tensors in module with those in new_params, while
`_load_from_state_dict()` loads only the value. Use this function so
that the `grad` and `grad_fn` of `new_params` can be restored
A... | update_module_params | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def soft_update_model(target_model, source_model, tau):
"""Update model parameter of target and source model.
# noqa: D417
Args:
target_model
(garage.torch.Policy/garage.torch.QFunction):
Target model to update.
source_model
(garage.torch.... | Update model parameter of target and source model.
# noqa: D417
Args:
target_model
(garage.torch.Policy/garage.torch.QFunction):
Target model to update.
source_model
(garage.torch.Policy/QFunction):
Source network to update... | soft_update_model | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def set_gpu_mode(mode, gpu_id=0):
"""Set GPU mode and device ID.
Args:
mode (bool): Whether or not to use GPU
gpu_id (int): GPU ID
"""
# pylint: disable=global-statement
global _GPU_ID
global _USE_GPU
global _DEVICE
_GPU_ID = gpu_id
_USE_GPU = mode
_DEVICE = tor... | Set GPU mode and device ID.
Args:
mode (bool): Whether or not to use GPU
gpu_id (int): GPU ID
| set_gpu_mode | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def prefer_gpu():
"""Prefer to use GPU(s) if GPU(s) is detected."""
if torch.cuda.is_available():
set_gpu_mode(True)
else:
set_gpu_mode(False) | Prefer to use GPU(s) if GPU(s) is detected. | prefer_gpu | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def global_device():
"""Returns the global device that torch.Tensors should be placed on.
Note: The global device is set by using the function
`garage.torch._functions.set_gpu_mode.`
If this functions is never called
`garage.torch._functions.device()` returns None.
Returns:
... | Returns the global device that torch.Tensors should be placed on.
Note: The global device is set by using the function
`garage.torch._functions.set_gpu_mode.`
If this functions is never called
`garage.torch._functions.device()` returns None.
Returns:
`torch.Device`: The global ... | global_device | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def product_of_gaussians(mus, sigmas_squared):
"""Compute mu, sigma of product of gaussians.
Args:
mus (torch.Tensor): Means, with shape :math:`(N, M)`. M is the number
of mean values.
sigmas_squared (torch.Tensor): Variances, with shape :math:`(N, V)`. V
is the number o... | Compute mu, sigma of product of gaussians.
Args:
mus (torch.Tensor): Means, with shape :math:`(N, M)`. M is the number
of mean values.
sigmas_squared (torch.Tensor): Variances, with shape :math:`(N, V)`. V
is the number of variance values.
Returns:
torch.Tensor:... | product_of_gaussians | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def state_dict_to(state_dict, device):
"""Move optimizer to a specified device.
Args:
state_dict (dict): state dictionary to be moved
device (str): ID of GPU or CPU.
Returns:
dict: state dictionary moved to device
"""
for param in state_dict.values():
if isinstance(... | Move optimizer to a specified device.
Args:
state_dict (dict): state dictionary to be moved
device (str): ID of GPU or CPU.
Returns:
dict: state dictionary moved to device
| state_dict_to | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def _value_at_axis(value, axis):
"""Get the value for a particular axis.
Args:
value (tuple or list or int): Possible tuple of per-axis values.
axis (int): Axis to get value for.
Returns:
int: the value at the available axis.
"""
if not isinstance(value, (list, tuple)):
... | Get the value for a particular axis.
Args:
value (tuple or list or int): Possible tuple of per-axis values.
axis (int): Axis to get value for.
Returns:
int: the value at the available axis.
| _value_at_axis | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def output_height_2d(layer, height):
"""Compute the output height of a torch.nn.Conv2d, assuming NCHW format.
This requires knowing the input height. Because NCHW format makes this very
easy to mix up, this is a seperate function from conv2d_output_height.
It also works on torch.nn.MaxPool2d.
Thi... | Compute the output height of a torch.nn.Conv2d, assuming NCHW format.
This requires knowing the input height. Because NCHW format makes this very
easy to mix up, this is a seperate function from conv2d_output_height.
It also works on torch.nn.MaxPool2d.
This function implements the formula described ... | output_height_2d | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def output_width_2d(layer, width):
"""Compute the output width of a torch.nn.Conv2d, assuming NCHW format.
This requires knowing the input width. Because NCHW format makes this very
easy to mix up, this is a seperate function from conv2d_output_height.
It also works on torch.nn.MaxPool2d.
This fu... | Compute the output width of a torch.nn.Conv2d, assuming NCHW format.
This requires knowing the input width. Because NCHW format makes this very
easy to mix up, this is a seperate function from conv2d_output_height.
It also works on torch.nn.MaxPool2d.
This function implements the formula described in... | output_width_2d | python | rlworkgroup/garage | src/garage/torch/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/_functions.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, for services such as
snapshotting and sampler control.
"""
if not self._eval_env:
self._eval_env = trainer.get_env... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, for services such as
snapshotting and sampler control.
| train | python | rlworkgroup/garage | src/garage/torch/algos/bc.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/bc.py | MIT |
def _train_once(self, trainer, epoch):
"""Obtain samplers and train for one epoch.
Args:
trainer (Trainer): Experiment trainer, which may be used to
obtain samples.
epoch (int): The current epoch.
Returns:
List[float]: Losses.
"""
... | Obtain samplers and train for one epoch.
Args:
trainer (Trainer): Experiment trainer, which may be used to
obtain samples.
epoch (int): The current epoch.
Returns:
List[float]: Losses.
| _train_once | python | rlworkgroup/garage | src/garage/torch/algos/bc.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/bc.py | MIT |
def _obtain_samples(self, trainer, epoch):
"""Obtain samples from self._source.
Args:
trainer (Trainer): Experiment trainer, which may be used to
obtain samples.
epoch (int): The current epoch.
Returns:
TimeStepBatch: Batch of samples.
... | Obtain samples from self._source.
Args:
trainer (Trainer): Experiment trainer, which may be used to
obtain samples.
epoch (int): The current epoch.
Returns:
TimeStepBatch: Batch of samples.
| _obtain_samples | python | rlworkgroup/garage | src/garage/torch/algos/bc.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/bc.py | MIT |
def _compute_loss(self, observations, expert_actions):
"""Compute loss of self._learner on the expert_actions.
Args:
observations (torch.Tensor): Observations used to select actions.
Has shape :math:`(B, O^*)`, where :math:`B` is the batch
dimension and :math... | Compute loss of self._learner on the expert_actions.
Args:
observations (torch.Tensor): Observations used to select actions.
Has shape :math:`(B, O^*)`, where :math:`B` is the batch
dimension and :math:`O^*` are the observation dimensions.
expert_actions ... | _compute_loss | python | rlworkgroup/garage | src/garage/torch/algos/bc.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/bc.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer.
Returns:
float: The average return in last epoch cycle.
"""
if not self._eval_env:
self._eval_env = trainer.g... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/torch/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ddpg.py | MIT |
def train_once(self, itr, episodes):
"""Perform one iteration of training.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
"""
self.replay_buffer.add_episode_batch(episodes)
epoch = itr / self._steps_per_epoch
for... | Perform one iteration of training.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
| train_once | python | rlworkgroup/garage | src/garage/torch/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ddpg.py | MIT |
def optimize_policy(self, samples_data):
"""Perform algorithm optimizing.
Args:
samples_data (dict): Processed batch data.
Returns:
action_loss: Loss of action predicted by the policy network.
qval_loss: Loss of Q-value predicted by the Q-network.
... | Perform algorithm optimizing.
Args:
samples_data (dict): Processed batch data.
Returns:
action_loss: Loss of action predicted by the policy network.
qval_loss: Loss of Q-value predicted by the Q-network.
ys: y_s.
qval: Q-value predicted by th... | optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ddpg.py | MIT |
def update_target(self):
"""Update parameters in the target policy and Q-value network."""
for t_param, param in zip(self._target_qf.parameters(),
self._qf.parameters()):
t_param.data.copy_(t_param.data * (1.0 - self._tau) +
pa... | Update parameters in the target policy and Q-value network. | update_target | python | rlworkgroup/garage | src/garage/torch/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ddpg.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer.
Returns:
float: The average return in last epoch cycle.
"""
if not self._eval_env:
self._eval_env = trainer.g... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/torch/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/dqn.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one iteration of training.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
"""
self.replay_buffer.add_episode_batch(episodes)
epoch = itr / self._steps_per_epoch
fo... | Perform one iteration of training.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
| _train_once | python | rlworkgroup/garage | src/garage/torch/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/dqn.py | MIT |
def _log_eval_results(self, epoch):
"""Log evaluation results after an epoch.
Args:
epoch (int): Current epoch.
"""
logger.log('Training finished')
if self.replay_buffer.n_transitions_stored >= self._min_buffer_size:
tabular.record('Epoch', epoch)
... | Log evaluation results after an epoch.
Args:
epoch (int): Current epoch.
| _log_eval_results | python | rlworkgroup/garage | src/garage/torch/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/dqn.py | MIT |
def _optimize_qf(self, timesteps):
"""Perform algorithm optimizing.
Args:
timesteps (TimeStepBatch): Processed batch data.
Returns:
qval_loss: Loss of Q-value predicted by the Q-network.
ys: y_s.
qval: Q-value predicted by the Q-network.
... | Perform algorithm optimizing.
Args:
timesteps (TimeStepBatch): Processed batch data.
Returns:
qval_loss: Loss of Q-value predicted by the Q-network.
ys: y_s.
qval: Q-value predicted by the Q-network.
| _optimize_qf | python | rlworkgroup/garage | src/garage/torch/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/dqn.py | MIT |
def to(self, device=None):
"""Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
"""
if device is None:
device = global_device()
logger.log('Using device: ' + str(device))
self._qf = self._qf.to(device)
... | Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
| to | python | rlworkgroup/garage | src/garage/torch/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/dqn.py | MIT |
def train(self, trainer):
"""Obtain samples and start training for each epoch.
Args:
trainer (Trainer): Gives the algorithm access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Returns:
... | Obtain samples and start training for each epoch.
Args:
trainer (Trainer): Gives the algorithm access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in las... | train | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _train_once(self, trainer, all_samples, all_params):
"""Train the algorithm once.
Args:
trainer (Trainer): The experiment runner.
all_samples (list[list[_MAMLEpisodeBatch]]): A two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size... | Train the algorithm once.
Args:
trainer (Trainer): The experiment runner.
all_samples (list[list[_MAMLEpisodeBatch]]): A two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size * (num_grad_updates + 1)]
all_params (list[dict]): A li... | _train_once | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _train_value_function(self, paths):
"""Train the value function.
Args:
paths (list[dict]): A list of collected paths.
Returns:
torch.Tensor: Calculated mean scalar value of value function loss
(float).
"""
# MAML resets a value funct... | Train the value function.
Args:
paths (list[dict]): A list of collected paths.
Returns:
torch.Tensor: Calculated mean scalar value of value function loss
(float).
| _train_value_function | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _obtain_samples(self, trainer):
"""Obtain samples for each task before and after the fast-adaptation.
Args:
trainer (Trainer): A trainer instance to obtain samples.
Returns:
tuple: Tuple of (all_samples, all_params).
all_samples (list[_MAMLEpisodeBat... | Obtain samples for each task before and after the fast-adaptation.
Args:
trainer (Trainer): A trainer instance to obtain samples.
Returns:
tuple: Tuple of (all_samples, all_params).
all_samples (list[_MAMLEpisodeBatch]): A list of size
[meta_... | _obtain_samples | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _adapt(self, batch_samples, set_grad=True):
"""Performs one MAML inner step to update the policy.
Args:
batch_samples (_MAMLEpisodeBatch): Samples data for one
task and one gradient step.
set_grad (bool): if False, update policy parameters in-place.
... | Performs one MAML inner step to update the policy.
Args:
batch_samples (_MAMLEpisodeBatch): Samples data for one
task and one gradient step.
set_grad (bool): if False, update policy parameters in-place.
Else, allow taking gradient of functions of updated ... | _adapt | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _compute_meta_loss(self, all_samples, all_params, set_grad=True):
"""Compute loss to meta-optimize.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): A two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size * (num_grad_updates + 1)]
... | Compute loss to meta-optimize.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): A two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size * (num_grad_updates + 1)]
all_params (list[dict]): A list of named parameter dictionaries.
... | _compute_meta_loss | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
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 Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
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.