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 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 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 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 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 _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 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 _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 |
def _compute_kl_constraint(self, all_samples, all_params, set_grad=True):
"""Compute KL divergence.
For each task, compute the KL divergence between the old policy
distribution and current policy distribution.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): Two
... | Compute KL divergence.
For each task, compute the KL divergence between the old policy
distribution and current policy distribution.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size... | _compute_kl_constraint | 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_policy_entropy(self, task_samples):
"""Compute policy entropy.
Args:
task_samples (list[_MAMLEpisodeBatch]): Samples data for
one task.
Returns:
torch.Tensor: Computed entropy value.
"""
obs = torch.cat([samples.observations... | Compute policy entropy.
Args:
task_samples (list[_MAMLEpisodeBatch]): Samples data for
one task.
Returns:
torch.Tensor: Computed entropy value.
| _compute_policy_entropy | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _process_samples(self, episodes):
"""Process sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Collected batch of episodes.
Returns:
_MAMLEpisodeBatch: Processed samples data.
"""
paths = episodes.to_list()
for path in... | Process sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Collected batch of episodes.
Returns:
_MAMLEpisodeBatch: Processed samples data.
| _process_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 _log_performance(self, itr, all_samples, loss_before, loss_after,
kl_before, kl, policy_entropy):
"""Evaluate performance of this batch.
Args:
itr (int): Iteration number.
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional l... | Evaluate performance of this batch.
Args:
itr (int): Iteration number.
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size * (num_grad_updates + 1)]
loss_before (float): Loss before o... | _log_performance | 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_policy(self, exploration_policy, exploration_episodes):
"""Adapt the policy by one gradient steps for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by i... | Adapt the policy by one gradient steps for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting with an environment.
The caller may not use this o... | adapt_policy | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _get_log_alpha(self, samples_data):
"""Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.... | Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
samples_data's entries ... | _get_log_alpha | python | rlworkgroup/garage | src/garage/torch/algos/mtsac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/mtsac.py | MIT |
def _evaluate_policy(self, epoch):
"""Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float:... | Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float: The average return across self._num_evaluatio... | _evaluate_policy | python | rlworkgroup/garage | src/garage/torch/algos/mtsac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/mtsac.py | MIT |
def train(self, trainer):
"""Obtain samples, train, and evaluate for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`Trainer..step_epochs()`, which provides services
such as snapshotting and sampler control.
"""
... | Obtain samples, train, and evaluate for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`Trainer..step_epochs()`, which provides services
such as snapshotting and sampler control.
| train | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _optimize_policy(self, indices):
"""Perform algorithm optimizing.
Args:
indices (list): Tasks used for training.
"""
num_tasks = len(indices)
context = self._sample_context(indices)
# clear context and reset belief of policy
self._policy.reset_be... | Perform algorithm optimizing.
Args:
indices (list): Tasks used for training.
| _optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _obtain_samples(self,
trainer,
itr,
num_samples,
update_posterior_rate,
add_to_enc_buffer=True):
"""Obtain samples.
Args:
trainer (Trainer): Trainer.
itr (... | Obtain samples.
Args:
trainer (Trainer): Trainer.
itr (int): Index of iteration (epoch).
num_samples (int): Number of samples to obtain.
update_posterior_rate (int): How often (in episodes) to infer
posterior of policy.
add_to_enc_buff... | _obtain_samples | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _sample_data(self, indices):
"""Sample batch of training data from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Obervations, with shape :math:`(X, N, O^*)` where X
is the number of tasks. N is bat... | Sample batch of training data from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Obervations, with shape :math:`(X, N, O^*)` where X
is the number of tasks. N is batch size.
torch.Tensor: Actions, ... | _sample_data | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _sample_context(self, indices):
"""Sample batch of context from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Context data, with shape :math:`(X, N, C)`. X is the
number of tasks. N is batch size. ... | Sample batch of context from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Context data, with shape :math:`(X, N, C)`. X is the
number of tasks. N is batch size. C is the combined size of
obser... | _sample_context | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _update_target_network(self):
"""Update parameters in the target vf network."""
for target_param, param in zip(self.target_vf.parameters(),
self._vf.parameters()):
target_param.data.copy_(target_param.data *
(1.0 ... | Update parameters in the target vf network. | _update_target_network | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def networks(self):
"""Return all the networks within the model.
Returns:
list: A list of networks.
"""
return self._policy.networks + [self._policy] + [
self._qf1, self._qf2, self._vf, self.target_vf
] | Return all the networks within the model.
Returns:
list: A list of networks.
| networks | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def adapt_policy(self, exploration_policy, exploration_episodes):
"""Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting wit... | Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting with an environment.
The caller may not use this object after pa... | adapt_policy | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def augment_env_spec(cls, env_spec, latent_dim):
"""Augment environment by a size of latent dimension.
Args:
env_spec (EnvSpec): Environment specs to be augmented.
latent_dim (int): Latent dimension.
Returns:
EnvSpec: Augmented environment specs.
""... | Augment environment by a size of latent dimension.
Args:
env_spec (EnvSpec): Environment specs to be augmented.
latent_dim (int): Latent dimension.
Returns:
EnvSpec: Augmented environment specs.
| augment_env_spec | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def get_env_spec(cls, env_spec, latent_dim, module):
"""Get environment specs of encoder with latent dimension.
Args:
env_spec (EnvSpec): Environment specification.
latent_dim (int): Latent dimension.
module (str): Module to get environment specs for.
Return... | Get environment specs of encoder with latent dimension.
Args:
env_spec (EnvSpec): Environment specification.
latent_dim (int): Latent dimension.
module (str): Module to get environment specs for.
Returns:
InOutSpec: Module environment specs with latent d... | get_env_spec | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _compute_objective(self, advantages, obs, actions, rewards):
r"""Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
wit... | Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions... | _compute_objective | python | rlworkgroup/garage | src/garage/torch/algos/ppo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ppo.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Retu... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Returns:
float: The average r... | train | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def train_once(self, itr=None, paths=None):
"""Complete 1 training iteration of SAC.
Args:
itr (int): Iteration number. This argument is deprecated.
paths (list[dict]): A list of collected paths.
This argument is deprecated.
Returns:
torch.Te... | Complete 1 training iteration of SAC.
Args:
itr (int): Iteration number. This argument is deprecated.
paths (list[dict]): A list of collected paths.
This argument is deprecated.
Returns:
torch.Tensor: loss from actor/policy network after optimization... | train_once | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _get_log_alpha(self, samples_data):
"""Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.... | Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
This function exists in case there are ve... | _get_log_alpha | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _temperature_objective(self, log_pi, samples_data):
"""Compute the temperature/alpha coefficient loss.
Args:
log_pi(torch.Tensor): log probability of actions that are sampled
from the replay buffer. Shape is (1, buffer_batch_size).
samples_data (dict): Transi... | Compute the temperature/alpha coefficient loss.
Args:
log_pi(torch.Tensor): log probability of actions that are sampled
from the replay buffer. Shape is (1, buffer_batch_size).
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay bu... | _temperature_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _actor_objective(self, samples_data, new_actions, log_pi_new_actions):
"""Compute the Policy/Actor loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', '... | Compute the Policy/Actor loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
new_actions (torch.Tensor): Actions re... | _actor_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _critic_objective(self, samples_data):
"""Compute the Q-function/critic loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observ... | Compute the Q-function/critic loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
samples_data's ent... | _critic_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _caps_regularization_objective(self, action_dists, samples_data):
"""Compute the spatial and temporal regularization loss as in CAPS.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
... | Compute the spatial and temporal regularization loss as in CAPS.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
acti... | _caps_regularization_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _update_targets(self):
"""Update parameters in the target q-functions."""
target_qfs = [self._target_qf1, self._target_qf2]
qfs = [self._qf1, self._qf2]
for target_qf, qf in zip(target_qfs, qfs):
for t_param, param in zip(target_qf.parameters(), qf.parameters()):
... | Update parameters in the target q-functions. | _update_targets | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def optimize_policy(self, samples_data):
"""Optimize the policy q_functions, and temperature coefficient.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'ter... | Optimize the policy q_functions, and temperature coefficient.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
... | optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _log_statistics(self, policy_loss, qf1_loss, qf2_loss):
"""Record training statistics to dowel such as losses and returns.
Args:
policy_loss (torch.Tensor): loss from actor/policy network.
qf1_loss (torch.Tensor): loss from 1st qf/critic network.
qf2_loss (torch.... | Record training statistics to dowel such as losses and returns.
Args:
policy_loss (torch.Tensor): loss from actor/policy network.
qf1_loss (torch.Tensor): loss from 1st qf/critic network.
qf2_loss (torch.Tensor): loss from 2nd qf/critic network.
| _log_statistics | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _get_action(self, action, noise_scale):
"""Select action based on policy.
Action can be added with noise.
Args:
action (float): Action.
noise_scale (float): Noise scale added to action.
Return:
float: Action selected by the policy.
"""
... | Select action based on policy.
Action can be added with noise.
Args:
action (float): Action.
noise_scale (float): Noise scale added to action.
Return:
float: Action selected by the policy.
| _get_action | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
"""
if not self._eval_env:
self._eval_env = trai... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
| train | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _train_once(self, itr):
"""Perform one iteration of training.
Args:
itr (int): Iteration number.
"""
for grad_step_timer in range(self._grad_steps_per_env_step):
if (self._replay_buffer.n_transitions_stored >=
self._min_buffer_size):
... | Perform one iteration of training.
Args:
itr (int): Iteration number.
| _train_once | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _optimize_policy(self, samples_data, grad_step_timer):
"""Perform algorithm optimization.
Args:
samples_data (dict): Processed batch data.
grad_step_timer (int): Iteration number of the gradient time
taken in the env.
Returns:
float: Loss... | Perform algorithm optimization.
Args:
samples_data (dict): Processed batch data.
grad_step_timer (int): Iteration number of the gradient time
taken in the env.
Returns:
float: Loss predicted by the q networks
(critic networks).
... | _optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _evaluate_policy(self):
"""Evaluate the performance of the policy via deterministic rollouts.
Statistics such as (average) discounted return and success rate are
recorded.
Returns:
TrajectoryBatch: Evaluation trajectories, representing the best
curre... | Evaluate the performance of the policy via deterministic rollouts.
Statistics such as (average) discounted return and success rate are
recorded.
Returns:
TrajectoryBatch: Evaluation trajectories, representing the best
current performance of the algorithm.
... | _evaluate_policy | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
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