code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def __getstate__(self):
"""Refuse to be pickled.
Raises:
ValueError: Always raised, since pickling Workers is not supported.
"""
raise ValueError('Workers are not pickleable. '
'Please pickle the WorkerFactory instead.') | Refuse to be pickled.
Raises:
ValueError: Always raised, since pickling Workers is not supported.
| __getstate__ | python | rlworkgroup/garage | src/garage/sampler/worker.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py | MIT |
def prepare_worker_messages(self, objs, preprocess=identity_function):
"""Take an argument and canonicalize it into a list for all workers.
This helper function is used to handle arguments in the sampler API
which may (optionally) be lists. Specifically, these are agent, env,
agent_upda... | Take an argument and canonicalize it into a list for all workers.
This helper function is used to handle arguments in the sampler API
which may (optionally) be lists. Specifically, these are agent, env,
agent_update, and env_update. Checks that the number of parameters is
correct.
... | prepare_worker_messages | python | rlworkgroup/garage | src/garage/sampler/worker_factory.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker_factory.py | MIT |
def __call__(self, worker_number):
"""Construct a worker given its number.
Args:
worker_number(int): The worker number. Should be at least 0 and
less than or equal to `n_workers`.
Raises:
ValueError: If the worker number is greater than `n_workers`.
... | Construct a worker given its number.
Args:
worker_number(int): The worker number. Should be at least 0 and
less than or equal to `n_workers`.
Raises:
ValueError: If the worker number is greater than `n_workers`.
Returns:
garage.sampler.Worke... | __call__ | python | rlworkgroup/garage | src/garage/sampler/worker_factory.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker_factory.py | MIT |
def step(self, action, agent_info):
"""Step the episode using an action from an agent.
Args:
action (np.ndarray): The action taken by the agent.
agent_info (dict[str, np.ndarray]): Extra agent information.
Returns:
np.ndarray: The new observation from the en... | Step the episode using an action from an agent.
Args:
action (np.ndarray): The action taken by the agent.
agent_info (dict[str, np.ndarray]): Extra agent information.
Returns:
np.ndarray: The new observation from the environment.
| step | python | rlworkgroup/garage | src/garage/sampler/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/_dtypes.py | MIT |
def to_batch(self):
"""Convert this in-progress episode into a EpisodeBatch.
Returns:
EpisodeBatch: This episode as a batch.
Raises:
AssertionError: If this episode contains no time steps.
"""
assert len(self.rewards) > 0
env_infos = dict(self.e... | Convert this in-progress episode into a EpisodeBatch.
Returns:
EpisodeBatch: This episode as a batch.
Raises:
AssertionError: If this episode contains no time steps.
| to_batch | python | rlworkgroup/garage | src/garage/sampler/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/_dtypes.py | MIT |
def _apply_env_update(old_env, env_update):
"""Use any non-None env_update as a new environment.
A simple env update function. If env_update is not None, it should be
the complete new environment.
This allows changing environments by passing the new environment as
`env_update` into `obtain_samples... | Use any non-None env_update as a new environment.
A simple env update function. If env_update is not None, it should be
the complete new environment.
This allows changing environments by passing the new environment as
`env_update` into `obtain_samples`.
Args:
old_env (Environment): Enviro... | _apply_env_update | python | rlworkgroup/garage | src/garage/sampler/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/_functions.py | MIT |
def compile_function(inputs, outputs):
"""Compiles a tensorflow function using the current session.
Args:
inputs (list[tf.Tensor]): Inputs to the function. Can be a list of
inputs or just one.
outputs (list[tf.Tensor]): Outputs of the function. Can be a list of
outputs o... | Compiles a tensorflow function using the current session.
Args:
inputs (list[tf.Tensor]): Inputs to the function. Can be a list of
inputs or just one.
outputs (list[tf.Tensor]): Outputs of the function. Can be a list of
outputs or just one.
Returns:
Callable: Co... | compile_function | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def get_target_ops(variables, target_variables, tau=None):
"""Get target variables update operations.
In RL algorithms we often update target network every n
steps. This function returns the tf.Operation for updating
target variables (denoted by target_var) from variables
(denote by var) with fract... | Get target variables update operations.
In RL algorithms we often update target network every n
steps. This function returns the tf.Operation for updating
target variables (denoted by target_var) from variables
(denote by var) with fraction tau. In other words, each time
we want to keep tau of the ... | get_target_ops | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def flatten_batch_dict(d, name='flatten_batch_dict'):
"""Flatten a batch of observations represented as a dict.
Args:
d (dict[tf.Tensor]): A dict of Tensors to flatten.
name (string): The name of the operation (None by default).
Returns:
dict[tf.Tensor]: A dict with flattened tenso... | Flatten a batch of observations represented as a dict.
Args:
d (dict[tf.Tensor]): A dict of Tensors to flatten.
name (string): The name of the operation (None by default).
Returns:
dict[tf.Tensor]: A dict with flattened tensors.
| flatten_batch_dict | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def filter_valids(t, valid, name='filter_valids'):
"""Filter out tensor using valid array.
Args:
t (tf.Tensor): The tensor to filter.
valid (list[float]): Array of length of the valid values (either
0 or 1).
name (string): Name of the operation.
Returns:
tf.Tens... | Filter out tensor using valid array.
Args:
t (tf.Tensor): The tensor to filter.
valid (list[float]): Array of length of the valid values (either
0 or 1).
name (string): Name of the operation.
Returns:
tf.Tensor: Filtered Tensor.
| filter_valids | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def filter_valids_dict(d, valid, name='filter_valids_dict'):
"""Filter valid values on a dict.
Args:
d (dict[tf.Tensor]): Dict of tensors to be filtered.
valid (list[float]): Array of length of the valid values (elements
can be either 0 or 1).
name (string): Name of the operatio... | Filter valid values on a dict.
Args:
d (dict[tf.Tensor]): Dict of tensors to be filtered.
valid (list[float]): Array of length of the valid values (elements
can be either 0 or 1).
name (string): Name of the operation. None by default.
Returns:
dict[tf.Tensor]: Dict with... | filter_valids_dict | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def flatten_inputs(deep):
"""Flattens an :class:`collections.abc.Iterable` recursively.
Args:
deep (Iterable): An :class:`~collections.abc.Iterable` to flatten.
Returns:
list: The flattened result.
"""
def flatten(deep):
# pylint: disable=missing-yield-doc,missing-yield-ty... | Flattens an :class:`collections.abc.Iterable` recursively.
Args:
deep (Iterable): An :class:`~collections.abc.Iterable` to flatten.
Returns:
list: The flattened result.
| flatten_inputs | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def new_tensor(name, ndim, dtype):
"""Creates a placeholder :class:`tf.Tensor` with the specified arguments.
Args:
name (string): Name of the tf.Tensor.
ndim (int): Number of dimensions of the tf.Tensor.
dtype (type): Data type of the tf.Tensor's contents.
Returns:
tf.Tenso... | Creates a placeholder :class:`tf.Tensor` with the specified arguments.
Args:
name (string): Name of the tf.Tensor.
ndim (int): Number of dimensions of the tf.Tensor.
dtype (type): Data type of the tf.Tensor's contents.
Returns:
tf.Tensor: Placeholder tensor.
| new_tensor | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def concat_tensor_dict_list(tensor_dict_list):
"""Concatenates a dict of tensors lists.
Each list of tensors gets concatenated into one tensor.
Args:
tensor_dict_list (dict[list[ndarray]]): Dict with lists of tensors.
Returns:
dict[ndarray]: A dict with the concatenated tensors.
"... | Concatenates a dict of tensors lists.
Each list of tensors gets concatenated into one tensor.
Args:
tensor_dict_list (dict[list[ndarray]]): Dict with lists of tensors.
Returns:
dict[ndarray]: A dict with the concatenated tensors.
| concat_tensor_dict_list | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def stack_tensor_dict_list(tensor_dict_list):
"""Stack a list of dictionaries of {tensors or dictionary of tensors}.
Args:
tensor_dict_list (dict): a list of dictionaries of {tensors or
dictionary of tensors}.
Returns:
dict: a dictionary of {stacked tensors or dictionary of sta... | Stack a list of dictionaries of {tensors or dictionary of tensors}.
Args:
tensor_dict_list (dict): a list of dictionaries of {tensors or
dictionary of tensors}.
Returns:
dict: a dictionary of {stacked tensors or dictionary of stacked
tensors}.
| stack_tensor_dict_list | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def split_tensor_dict_list(tensor_dict):
"""Split a list of dictionaries of {tensors or dictionary of tensors}.
Args:
tensor_dict (dict): a list of dictionaries of {tensors or
dictionary of tensors}.
Returns:
dict: a dictionary of {split tensors or dictionary of split tensors}.
... | Split a list of dictionaries of {tensors or dictionary of tensors}.
Args:
tensor_dict (dict): a list of dictionaries of {tensors or
dictionary of tensors}.
Returns:
dict: a dictionary of {split tensors or dictionary of split tensors}.
| split_tensor_dict_list | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def pad_tensor(x, max_len):
"""Pad tensors with zeros.
Args:
x (numpy.ndarray): Tensors to be padded.
max_len (int): Maximum length.
Returns:
numpy.ndarray: Padded tensor.
"""
return np.concatenate([
x,
np.tile(np.zeros_like(x[0]),
(max_len -... | Pad tensors with zeros.
Args:
x (numpy.ndarray): Tensors to be padded.
max_len (int): Maximum length.
Returns:
numpy.ndarray: Padded tensor.
| pad_tensor | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def pad_tensor_n(xs, max_len):
"""Pad array of tensors.
Args:
xs (numpy.ndarray): Tensors to be padded.
max_len (int): Maximum length.
Returns:
numpy.ndarray: Padded tensor.
"""
ret = np.zeros((len(xs), max_len) + xs[0].shape[1:], dtype=xs[0].dtype)
for idx, x in enumer... | Pad array of tensors.
Args:
xs (numpy.ndarray): Tensors to be padded.
max_len (int): Maximum length.
Returns:
numpy.ndarray: Padded tensor.
| pad_tensor_n | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def pad_tensor_dict(tensor_dict, max_len):
"""Pad dictionary of tensors with zeros.
Args:
tensor_dict (dict[numpy.ndarray]): Tensors to be padded.
max_len (int): Maximum length.
Returns:
dict[numpy.ndarray]: Padded tensor.
"""
keys = list(tensor_dict.keys())
ret = dict(... | Pad dictionary of tensors with zeros.
Args:
tensor_dict (dict[numpy.ndarray]): Tensors to be padded.
max_len (int): Maximum length.
Returns:
dict[numpy.ndarray]: Padded tensor.
| pad_tensor_dict | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def compute_advantages(discount,
gae_lambda,
max_len,
baselines,
rewards,
name='compute_advantages'):
"""Calculate advantages.
Advantages are a discounted cumulative sum.
The discount cumulat... | Calculate advantages.
Advantages are a discounted cumulative sum.
The discount cumulative sum can be represented as an IIR
filter ob the reversed input vectors, i.e.
y[t] - discount*y[t+1] = x[t], or
rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]
Given the time-domain IIR filter step response,... | compute_advantages | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def center_advs(advs, axes, eps, offset=0, scale=1, name='center_adv'):
"""Normalize the advs tensor.
This calculates the mean and variance using the axes specified
and normalizes the tensor using those values.
Args:
advs (tf.Tensor): Tensor to normalize.
axes (array[int]): Axes along ... | Normalize the advs tensor.
This calculates the mean and variance using the axes specified
and normalizes the tensor using those values.
Args:
advs (tf.Tensor): Tensor to normalize.
axes (array[int]): Axes along which to compute the mean and variance.
eps (float): Small number to av... | center_advs | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def positive_advs(advs, eps, name='positive_adv'):
"""Make all the values in the advs tensor positive.
Offsets all values in advs by the minimum value in
the tensor, plus an epsilon value to avoid dividing by zero.
Args:
advs (tf.Tensor): The tensor to offset.
eps (tf.float32): A small... | Make all the values in the advs tensor positive.
Offsets all values in advs by the minimum value in
the tensor, plus an epsilon value to avoid dividing by zero.
Args:
advs (tf.Tensor): The tensor to offset.
eps (tf.float32): A small value to avoid by-zero division.
name (string): N... | positive_advs | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def discounted_returns(discount, max_len, rewards, name='discounted_returns'):
"""Calculate discounted returns.
Args:
discount (float): Discount factor.
max_len (int): Maximum length of a single episode.
rewards (tf.Tensor): A 2D vector of per-step rewards with shape
:math:`... | Calculate discounted returns.
Args:
discount (float): Discount factor.
max_len (int): Maximum length of a single episode.
rewards (tf.Tensor): A 2D vector of per-step rewards with shape
:math:`(N, T)`, where :math:`N` is the batch dimension (number of
episodes) and :... | discounted_returns | python | rlworkgroup/garage | src/garage/tf/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/_functions.py | MIT |
def _init_opt(self):
"""Build the loss function and init the optimizer."""
with tf.name_scope(self._name):
# Create target policy and qf network
with tf.name_scope('inputs'):
obs_dim = self._env_spec.observation_space.flat_dim
input_y = tf.compat.v... | Build the loss function and init the optimizer. | _init_opt | python | rlworkgroup/garage | src/garage/tf/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/ddpg.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
data = self.__dict__.copy()
del data['_target_policy_f_prob_online']
del data['_target_qf_f_prob_online']
del data['_f_train_policy']
... | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/ddpg.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.
Returns:
float: The average return in last epoch cycle.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/tf/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/ddpg.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
"""
self._replay_buffer.add_episode_batch(episodes)
epoch = itr / ... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/ddpg.py | MIT |
def _optimize_policy(self):
"""Perform algorithm optimizing.
Returns:
float: Loss of action predicted by the policy network
float: Loss of q value predicted by the q network.
float: ys.
float: Q value predicted by the q network.
"""
times... | Perform algorithm optimizing.
Returns:
float: Loss of action predicted by the policy network
float: Loss of q value predicted by the q network.
float: ys.
float: Q value predicted by the q network.
| _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/ddpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/ddpg.py | MIT |
def _init_opt(self):
"""Initialize the networks and Ops.
Assume discrete space for dqn, so action dimension
will always be action_space.n
"""
action_dim = self._env_spec.action_space.n
# build q networks
with tf.name_scope(self._name):
action_t_ph = ... | Initialize the networks and Ops.
Assume discrete space for dqn, so action dimension
will always be action_space.n
| _init_opt | python | rlworkgroup/garage | src/garage/tf/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/dqn.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.
Returns:
float: The average return in last epoch cycle.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/tf/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/dqn.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
list[float]: Q function losses
"""
self._rep... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
list[float]: Q function losses
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/dqn.py | MIT |
def _optimize_policy(self):
"""Optimize network using experiences from replay buffer.
Returns:
numpy.float64: Loss of policy.
"""
timesteps = self._replay_buffer.sample_timesteps(
self._buffer_batch_size)
observations = timesteps.observations
re... | Optimize network using experiences from replay buffer.
Returns:
numpy.float64: Loss of policy.
| _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/dqn.py | MIT |
def __getstate__(self):
"""Parameters to save in snapshot.
Returns:
dict: Parameters to save.
"""
data = self.__dict__.copy()
del data['_qf_update_ops']
del data['_train_qf']
return data | Parameters to save in snapshot.
Returns:
dict: Parameters to save.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/algos/dqn.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/dqn.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which rovides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which rovides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
"""
# -- Stag... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _optimize_policy(self, episodes, baselines):
"""Optimize policy.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
"""
policy_opt_input_values = self._policy_opt_input_values(
episodes, baselines)
... | Optimize policy.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
| _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _build_inputs(self):
"""Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
"""
observation_space = self.policy.observation_space
action_space = se... | Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
| _build_inputs | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _build_policy_loss(self, i):
"""Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
"""
policy_entropy =... | Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
| _build_policy_loss | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _build_entropy_term(self, i):
"""Build policy entropy tensor.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy entropy.
"""
pol_dist = self._policy_network.dist
with tf.name_scope('policy_entr... | Build policy entropy tensor.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy entropy.
| _build_entropy_term | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _fit_baseline_with_data(self, episodes, baselines):
"""Update baselines from samples.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
Returns:
np.ndarray: Augment returns.
"""
policy_opt_in... | Update baselines from samples.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
Returns:
np.ndarray: Augment returns.
| _fit_baseline_with_data | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _policy_opt_input_values(self, episodes, baselines):
"""Map episode samples to the policy optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
Returns:
list(np.ndarray): Flatten policy optimiza... | Map episode samples to the policy optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
Returns:
list(np.ndarray): Flatten policy optimization input values.
| _policy_opt_input_values | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def _check_entropy_configuration(self, entropy_method, center_adv,
stop_entropy_gradient,
use_neg_logli_entropy, policy_ent_coeff):
"""Check entropy configuration.
Args:
entropy_method (str): A string from: 'max', 're... | Check entropy configuration.
Args:
entropy_method (str): A string from: 'max', 'regularized',
'no_entropy'. The type of entropy method to use. 'max' adds the
dense entropy to the reward for each time step. 'regularized'
adds the mean entropy to the su... | _check_entropy_configuration | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def __getstate__(self):
"""Parameters to save in snapshot.
Returns:
dict: Parameters to save.
"""
data = self.__dict__.copy()
del data['_name_scope']
del data['_policy_opt_inputs']
del data['_f_policy_entropy']
del data['_f_policy_kl']
... | Parameters to save in snapshot.
Returns:
dict: Parameters to save.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.py | MIT |
def __setstate__(self, state):
"""Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
"""
self.__dict__ = state
self._name_scope = tf.name_scope(self._name)
self._init_opt() | Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
| __setstate__ | python | rlworkgroup/garage | src/garage/tf/algos/npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/npo.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.
Returns:
float: The average return in last epoch cycle.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
"""
# -- Stag... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def __getstate__(self):
"""Parameters to save in snapshot.
Returns:
dict: Parameters to save.
"""
data = self.__dict__.copy()
del data['_name_scope']
del data['_policy_opt_inputs']
del data['_dual_opt_inputs']
del data['_f_dual']
del ... | Parameters to save in snapshot.
Returns:
dict: Parameters to save.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def __setstate__(self, state):
"""Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
"""
self.__dict__ = state
self._name_scope = tf.name_scope(self._name)
self._init_opt() | Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
| __setstate__ | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _optimize_policy(self, episodes):
"""Optimize the policy using the samples.
Args:
episodes (EpisodeBatch): Batch of episodes.
"""
# Initial BFGS parameter values.
x0 = np.hstack([self._param_eta, self._param_v])
# Set parameter boundaries: \eta>=1e-12, v... | Optimize the policy using the samples.
Args:
episodes (EpisodeBatch): Batch of episodes.
| _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def eval_dual(x):
"""Evaluate dual function loss.
Args:
x (numpy.ndarray): Input to dual function.
Returns:
numpy.float64: Dual function loss.
"""
self._param_eta = x[0]
self._param_v = x[1:]
dual_opt_... | Evaluate dual function loss.
Args:
x (numpy.ndarray): Input to dual function.
Returns:
numpy.float64: Dual function loss.
| eval_dual | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def eval_dual_grad(x):
"""Evaluate gradient of dual function loss.
Args:
x (numpy.ndarray): Input to dual function.
Returns:
numpy.ndarray: Gradient of dual function loss.
"""
self._param_eta = x[0]
self._param_v ... | Evaluate gradient of dual function loss.
Args:
x (numpy.ndarray): Input to dual function.
Returns:
numpy.ndarray: Gradient of dual function loss.
| eval_dual_grad | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _build_inputs(self):
"""Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
"""
observation_space = self.policy.observation_space
action_space = se... | Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
| _build_inputs | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _build_policy_loss(self, i):
"""Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
Raises:
NotImple... | Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
Raises:
NotImplementedError: If is_recurrent is True.
... | _build_policy_loss | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _dual_opt_input_values(self, episodes):
"""Update dual func optimize input values based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
list(np.ndarray): Flatten dual function optimization input values.
"""
agent_infos = ... | Update dual func optimize input values based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
list(np.ndarray): Flatten dual function optimization input values.
| _dual_opt_input_values | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _policy_opt_input_values(self, episodes):
"""Update policy optimize input values based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
list(np.ndarray): Flatten policy optimization input values.
"""
agent_infos = episodes... | Update policy optimize input values based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
list(np.ndarray): Flatten policy optimization input values.
| _policy_opt_input_values | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def _features(self, episodes):
"""Get valid view features based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.ndarray: Features for training.
"""
start = 0
feat_diff = []
for length in episodes.lengths... | Get valid view features based on samples data.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.ndarray: Features for training.
| _features | python | rlworkgroup/garage | src/garage/tf/algos/reps.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/reps.py | MIT |
def step(self, action):
"""Call step on wrapped env.
Args:
action (np.ndarray): An action provided by the agent.
Returns:
EnvStep: The environment step resulting from the action.
Raises:
RuntimeError: if `step()` is called after the environment has ... | Call step on wrapped env.
Args:
action (np.ndarray): An action provided by the agent.
Returns:
EnvStep: The environment step resulting from the action.
Raises:
RuntimeError: if `step()` is called after the environment has been
constructed an... | step | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def _create_rl2_obs_space(self):
"""Create observation space for RL2.
Returns:
akro.Box: Augmented observation space.
"""
obs_flat_dim = np.prod(self._env.observation_space.shape)
action_flat_dim = np.prod(self._env.action_space.shape)
return akro.Box(low=-n... | Create observation space for RL2.
Returns:
akro.Box: Augmented observation space.
| _create_rl2_obs_space | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def rollout(self):
"""Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
"""
self.agent.reset()
for _ in range(self._n_episodes_per_trial):
self.start_episode()
while not self.step_episode()... | Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
| rollout | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def set_param_values(self, params):
"""Set param values.
Args:
params (Tuple[np.ndarray, np.ndarray]): Two numpy array of
parameter values, one of the network parameters, one
for the initial hidden state.
"""
inner_params, hiddens = params
... | Set param values.
Args:
params (Tuple[np.ndarray, np.ndarray]): Two numpy array of
parameter values, one of the network parameters, one
for the initial hidden state.
| set_param_values | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.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.
Returns:
float: The average return in last epoch.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch.
| train | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
"""
episodes, ... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
| train_once | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def _process_samples(self, itr, episodes):
# pylint: disable=too-many-statements
"""Return processed sample data based on the collected paths.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Original collected episode batch for each
task. For ... | Return processed sample data based on the collected paths.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Original collected episode batch for each
task. For each episode, episode.agent_infos['batch_idx']
indicates which task this episode bel... | _process_samples | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def _concatenate_episodes(self, episode_list):
"""Concatenate episodes.
The input list contains samples from different episodes but same
task/environment. In RL^2, paths within each meta batch are all
concatenate into a single path and fed to the policy.
Args:
episo... | Concatenate episodes.
The input list contains samples from different episodes but same
task/environment. In RL^2, paths within each meta batch are all
concatenate into a single path and fed to the policy.
Args:
episode_list (list[EpisodeBatch]): Input paths. All paths are f... | _concatenate_episodes | python | rlworkgroup/garage | src/garage/tf/algos/rl2.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/rl2.py | MIT |
def _init_opt(self):
"""Build the loss function and init the optimizer."""
with tf.name_scope(self._name):
# Create target policy (actor) and qf (critic) networks
with tf.name_scope('inputs'):
obs_dim = self._env_spec.observation_space.flat_dim
y =... | Build the loss function and init the optimizer. | _init_opt | python | rlworkgroup/garage | src/garage/tf/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/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.
Returns:
float: The average return in last epoch cycle.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
Returns:
float: The average return in last epoch cycle.
| train | python | rlworkgroup/garage | src/garage/tf/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/td3.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
"""
self._replay_buffer.add_episode_batch(episodes)
epoch = itr /... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/td3.py | MIT |
def _optimize_policy(self, itr):
"""Perform algorithm optimizing.
Args:
itr(int): Iterations.
Returns:
float: Loss of action predicted by the policy network.
float: Loss of q value predicted by the q network.
float: y_s.
float: Q valu... | Perform algorithm optimizing.
Args:
itr(int): Iterations.
Returns:
float: Loss of action predicted by the policy network.
float: Loss of q value predicted by the q network.
float: y_s.
float: Q value predicted by the q network.
| _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/td3.py | MIT |
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
"""
if self._eps_length < self._max_episode_... | Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
| step_episode | python | rlworkgroup/garage | src/garage/tf/algos/te.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te.py | MIT |
def collect_episode(self):
"""Collect the current episode, clearing the internal buffer.
One-hot task id is saved in env_infos['task_onehot']. Latent is saved
in agent_infos['latent']. Latent infos are saved in
agent_infos['latent_info_name'], where info_name is the original latent
... | Collect the current episode, clearing the internal buffer.
One-hot task id is saved in env_infos['task_onehot']. Latent is saved
in agent_infos['latent']. Latent infos are saved in
agent_infos['latent_info_name'], where info_name is the original latent
info name.
Returns:
... | collect_episode | python | rlworkgroup/garage | src/garage/tf/algos/te.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te.py | MIT |
def _init_opt(self):
"""Initialize optimizater.
Raises:
NotImplementedError: Raise if the policy is recurrent.
"""
# Input variables
(pol_loss_inputs, pol_opt_inputs, infer_loss_inputs,
infer_opt_inputs) = self._build_inputs()
self._policy_opt_inpu... | Initialize optimizater.
Raises:
NotImplementedError: Raise if the policy is recurrent.
| _init_opt | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
Returns:
float: The aver... | train | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _train_once(self, itr, episodes):
"""Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
"""
undiscoun... | Perform one step of policy optimization given one batch of samples.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
Returns:
numpy.float64: Average return.
| _train_once | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _optimize_policy(self, itr, episodes, baselines, embed_eps,
embed_ep_infos):
"""Optimize policy.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
embe... | Optimize policy.
Args:
itr (int): Iteration number.
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
embed_eps (np.ndarray): Embedding episodes.
embed_ep_infos (dict): Embedding distribution information.
... | _optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _process_episodes(self, episodes):
# pylint: disable=too-many-statements
"""Return processed sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
np.ndarray: Embedding episodes.
dict: Embedding dist... | Return processed sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
np.ndarray: Embedding episodes.
dict: Embedding distribution information.
* mean (list[numpy.ndarray]): Means of the distribution.
... | _process_episodes | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _build_inputs(self):
"""Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
namedtuple: Collection of variables to compute inference loss.
namedtupl... | Build input variables.
Returns:
namedtuple: Collection of variables to compute policy loss.
namedtuple: Collection of variables to do policy optimization.
namedtuple: Collection of variables to compute inference loss.
namedtuple: Collection of variables to do inf... | _build_inputs | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _build_policy_loss(self, i):
"""Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
"""
# pylint: disabl... | Build policy loss and other output tensors.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy loss.
tf.Tensor: Mean policy KL divergence.
| _build_policy_loss | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _build_entropy_terms(self, i):
"""Build policy entropy tensor.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy entropy.
"""
pol_dist = self._policy_network.dist
infer_dist = self._infer_networ... | Build policy entropy tensor.
Args:
i (namedtuple): Collection of variables to compute policy loss.
Returns:
tf.Tensor: Policy entropy.
| _build_entropy_terms | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _build_encoder_kl(self):
"""Build graph for encoder KL divergence.
Returns:
tf.Tensor: Encoder KL divergence.
"""
dist = self._encoder_network.dist
old_dist = self._old_encoder_network.dist
with tf.name_scope('encoder_kl'):
kl = old_dist.kl_... | Build graph for encoder KL divergence.
Returns:
tf.Tensor: Encoder KL divergence.
| _build_encoder_kl | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _build_inference_loss(self, i):
"""Build loss function for the inference network.
Args:
i (namedtuple): Collection of variables to compute inference loss.
Returns:
tf.Tensor: Inference loss.
"""
dist = self._infer_network.dist
old_dist = sel... | Build loss function for the inference network.
Args:
i (namedtuple): Collection of variables to compute inference loss.
Returns:
tf.Tensor: Inference loss.
| _build_inference_loss | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _policy_opt_input_values(self, episodes, baselines, embed_eps):
"""Map episode samples to the policy optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
embed_eps (np.ndarray): Embedding episodes.
... | Map episode samples to the policy optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
embed_eps (np.ndarray): Embedding episodes.
Returns:
list(np.ndarray): Flatten policy optimization input v... | _policy_opt_input_values | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _inference_opt_input_values(self, episodes, embed_eps, embed_ep_infos):
"""Map episode samples to the inference optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
embed_eps (np.ndarray): Embedding episodes.
embed_ep_infos (dict): Embedding distri... | Map episode samples to the inference optimizer inputs.
Args:
episodes (EpisodeBatch): Batch of episodes.
embed_eps (np.ndarray): Embedding episodes.
embed_ep_infos (dict): Embedding distribution information.
Returns:
list(np.ndarray): Flatten inference o... | _inference_opt_input_values | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _evaluate(self, policy_opt_input_values, episodes, baselines,
embed_ep_infos):
"""Evaluate rewards and everything else.
Args:
policy_opt_input_values (list[np.ndarray]): Flattened
policy optimization input values.
episodes (EpisodeBatch): Ba... | Evaluate rewards and everything else.
Args:
policy_opt_input_values (list[np.ndarray]): Flattened
policy optimization input values.
episodes (EpisodeBatch): Batch of episodes.
baselines (np.ndarray): Baseline predictions.
embed_ep_infos (dict): Em... | _evaluate | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _train_policy_and_encoder_networks(self, policy_opt_input_values):
"""Joint optimization of policy and encoder networks.
Args:
policy_opt_input_values (list(np.ndarray)): Flatten policy
optimization input values.
Returns:
float: Policy loss after opt... | Joint optimization of policy and encoder networks.
Args:
policy_opt_input_values (list(np.ndarray)): Flatten policy
optimization input values.
Returns:
float: Policy loss after optimization.
| _train_policy_and_encoder_networks | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _train_inference_network(self, inference_opt_input_values):
"""Optimize inference network.
Args:
inference_opt_input_values (list(np.ndarray)): Flatten inference
optimization input values.
Returns:
float: Inference loss after optmization.
""... | Optimize inference network.
Args:
inference_opt_input_values (list(np.ndarray)): Flatten inference
optimization input values.
Returns:
float: Inference loss after optmization.
| _train_inference_network | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def _get_latent_space(cls, latent_dim):
"""Get latent space given latent length.
Args:
latent_dim (int): Length of latent.
Returns:
akro.Space: Space of latent.
"""
latent_lb = np.zeros(latent_dim, )
latent_up = np.ones(latent_dim, )
ret... | Get latent space given latent length.
Args:
latent_dim (int): Length of latent.
Returns:
akro.Space: Space of latent.
| _get_latent_space | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def get_encoder_spec(cls, task_space, latent_dim):
"""Get the embedding spec of the encoder.
Args:
task_space (akro.Space): Task spec.
latent_dim (int): Latent dimension.
Returns:
garage.InOutSpec: Encoder spec.
"""
latent_space = cls._get_l... | Get the embedding spec of the encoder.
Args:
task_space (akro.Space): Task spec.
latent_dim (int): Latent dimension.
Returns:
garage.InOutSpec: Encoder spec.
| get_encoder_spec | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def get_infer_spec(cls, env_spec, latent_dim, inference_window_size):
"""Get the embedding spec of the inference.
Every `inference_window_size` timesteps in the trajectory will be used
as the inference network input.
Args:
env_spec (garage.envs.EnvSpec): Environment spec.
... | Get the embedding spec of the inference.
Every `inference_window_size` timesteps in the trajectory will be used
as the inference network input.
Args:
env_spec (garage.envs.EnvSpec): Environment spec.
latent_dim (int): Latent dimension.
inference_window_size ... | get_infer_spec | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def __getstate__(self):
"""Parameters to save in snapshot.
Returns:
dict: Parameters to save.
"""
data = self.__dict__.copy()
del data['_name_scope']
del data['_inference_opt_inputs']
del data['_policy_opt_inputs']
del data['_f_inference_ce']... | Parameters to save in snapshot.
Returns:
dict: Parameters to save.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def __setstate__(self, state):
"""Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
"""
self.__dict__ = state
self._name_scope = tf.name_scope(self._name)
self._init_opt() | Parameters to restore from snapshot.
Args:
state (dict): Parameters to restore from.
| __setstate__ | python | rlworkgroup/garage | src/garage/tf/algos/te_npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/te_npo.py | MIT |
def optimize_policy(self, episodes):
"""Optimize policy.
Args:
episodes (EpisodeBatch): Batch of episodes.
"""
# Baseline predictions with all zeros for feeding inputs of Tensorflow
baselines = np.zeros((len(episodes.lengths), max(episodes.lengths)))
return... | Optimize policy.
Args:
episodes (EpisodeBatch): Batch of episodes.
| optimize_policy | python | rlworkgroup/garage | src/garage/tf/algos/_rl2npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/_rl2npo.py | MIT |
def _get_baseline_prediction(self, episodes):
"""Get baseline prediction.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
np.ndarray: Baseline prediction, with shape
:math:`(N, max_episode_length * episode_per_task)`.
"""
obs ... | Get baseline prediction.
Args:
episodes (EpisodeBatch): Batch of episodes.
Returns:
np.ndarray: Baseline prediction, with shape
:math:`(N, max_episode_length * episode_per_task)`.
| _get_baseline_prediction | python | rlworkgroup/garage | src/garage/tf/algos/_rl2npo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/algos/_rl2npo.py | MIT |
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
"""
xs = np.concatenate([p['observations'] for p in paths])
if not isinstance(xs, np.ndarray) or len(xs.shape) > 2:
xs = self._env_spec.observation_sp... | Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
| fit | python | rlworkgroup/garage | src/garage/tf/baselines/continuous_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/continuous_mlp_baseline.py | MIT |
def predict(self, paths):
"""Predict value based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
obs = paths['observations']
if not isinstance(obs, np.ndarray) or len(obs.shape) > 2:
... | Predict value based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
| predict | python | rlworkgroup/garage | src/garage/tf/baselines/continuous_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/continuous_mlp_baseline.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_predict']
del new_dict['_x_mean']
del new_dict['_x_std']
del new_dict['_y_hat']
... | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/baselines/continuous_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/continuous_mlp_baseline.py | MIT |
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
"""
xs = np.concatenate([p['observations'] for p in paths])
if isinstance(self._env_spec.observation_space, akro.Image) and \
len(xs[0].shape) < \... | Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
| fit | python | rlworkgroup/garage | src/garage/tf/baselines/gaussian_cnn_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/gaussian_cnn_baseline.py | MIT |
def predict(self, paths):
"""Predict ys based on input xs.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Return:
numpy.ndarray: The predicted ys.
"""
xs = paths['observations']
if isinstance(self._env_spec.observation_space, akro.Image) and \... | Predict ys based on input xs.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Return:
numpy.ndarray: The predicted ys.
| predict | python | rlworkgroup/garage | src/garage/tf/baselines/gaussian_cnn_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/gaussian_cnn_baseline.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_predict']
del new_dict['_old_network']
del new_dict['_x_mean']
del new_dict['_x_st... | Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/tf/baselines/gaussian_cnn_baseline.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/gaussian_cnn_baseline.py | MIT |
def network_output_spec(self):
"""Network output spec.
Return:
list[str]: List of key(str) for the network outputs.
"""
return [
'sample', 'std_param', 'normalized_dist', 'normalized_mean',
'normalized_log_std', 'dist', 'mean', 'log_std', 'x_mean', '... | Network output spec.
Return:
list[str]: List of key(str) for the network outputs.
| network_output_spec | python | rlworkgroup/garage | src/garage/tf/baselines/gaussian_cnn_baseline_model.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/gaussian_cnn_baseline_model.py | MIT |
def _build(self, state_input, name=None):
"""Build model given input placeholder(s).
Args:
state_input (tf.Tensor): Place holder for state input.
name (str): Inner model name, also the variable scope of the
inner model, if exist. One example is
ga... | Build model given input placeholder(s).
Args:
state_input (tf.Tensor): Place holder for state input.
name (str): Inner model name, also the variable scope of the
inner model, if exist. One example is
garage.tf.models.Sequential.
Return:
... | _build | python | rlworkgroup/garage | src/garage/tf/baselines/gaussian_cnn_baseline_model.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/tf/baselines/gaussian_cnn_baseline_model.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.