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def from_env_step(cls, env_step, last_observation, agent_info,
episode_info):
"""Create a TimeStep from a EnvStep.
Args:
env_step (EnvStep): the env step returned by the environment.
last_observation (numpy.ndarray): A numpy array of shape
:... | Create a TimeStep from a EnvStep.
Args:
env_step (EnvStep): the env step returned by the environment.
last_observation (numpy.ndarray): A numpy array of shape
:math:`(O^*)` containing the observation for this time
step in the environment. These must confo... | from_env_step | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def concatenate(cls, *batches):
"""Concatenate two or more :class:`TimeStepBatch`s.
Args:
batches (list[TimeStepBatch]): Batches to concatenate.
Returns:
TimeStepBatch: The concatenation of the batches.
Raises:
ValueError: If no TimeStepBatches are ... | Concatenate two or more :class:`TimeStepBatch`s.
Args:
batches (list[TimeStepBatch]): Batches to concatenate.
Returns:
TimeStepBatch: The concatenation of the batches.
Raises:
ValueError: If no TimeStepBatches are provided.
| concatenate | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def split(self) -> List['TimeStepBatch']:
"""Split a :class:`~TimeStepBatch` into a list of :class:`~TimeStepBatch`s.
The opposite of concatenate.
Returns:
list[TimeStepBatch]: A list of :class:`TimeStepBatch`s, with one
:class:`~TimeStep` per :class:`~TimeStepBatch... | Split a :class:`~TimeStepBatch` into a list of :class:`~TimeStepBatch`s.
The opposite of concatenate.
Returns:
list[TimeStepBatch]: A list of :class:`TimeStepBatch`s, with one
:class:`~TimeStep` per :class:`~TimeStepBatch`.
| split | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def to_time_step_list(self) -> List[Dict[str, np.ndarray]]:
"""Convert the batch into a list of dictionaries.
Breaks the :class:`~TimeStepBatch` into a list of single time step
sample dictionaries. len(rewards) (or the number of discrete time step)
dictionaries are returned
Ret... | Convert the batch into a list of dictionaries.
Breaks the :class:`~TimeStepBatch` into a list of single time step
sample dictionaries. len(rewards) (or the number of discrete time step)
dictionaries are returned
Returns:
list[dict[str, np.ndarray or dict[str, np.ndarray]]]:... | to_time_step_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def from_time_step_list(cls, env_spec, ts_samples):
"""Create a :class:`~TimeStepBatch` from a list of time step dictionaries.
Args:
env_spec (EnvSpec): Specification for the environment from which
this data was sampled.
ts_samples (list[dict[str, np.ndarray or d... | Create a :class:`~TimeStepBatch` from a list of time step dictionaries.
Args:
env_spec (EnvSpec): Specification for the environment from which
this data was sampled.
ts_samples (list[dict[str, np.ndarray or dict[str, np.ndarray]]]):
keys:
... | from_time_step_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def concatenate(cls, *batches):
"""Create a EpisodeBatch by concatenating EpisodeBatches.
Args:
batches (list[EpisodeBatch]): Batches to concatenate.
Returns:
EpisodeBatch: The concatenation of the batches.
"""
if __debug__:
for b in batches... | Create a EpisodeBatch by concatenating EpisodeBatches.
Args:
batches (list[EpisodeBatch]): Batches to concatenate.
Returns:
EpisodeBatch: The concatenation of the batches.
| concatenate | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def _episode_ranges(self):
"""Iterate through start and stop indices for each episode.
Yields:
tuple[int, int]: Start index (inclusive) and stop index
(exclusive).
"""
start = 0
for length in self.lengths:
stop = start + length
... | Iterate through start and stop indices for each episode.
Yields:
tuple[int, int]: Start index (inclusive) and stop index
(exclusive).
| _episode_ranges | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def split(self):
"""Split an EpisodeBatch into a list of EpisodeBatches.
The opposite of concatenate.
Returns:
list[EpisodeBatch]: A list of EpisodeBatches, with one
episode per batch.
"""
episodes = []
for i, (start, stop) in enumerate(self... | Split an EpisodeBatch into a list of EpisodeBatches.
The opposite of concatenate.
Returns:
list[EpisodeBatch]: A list of EpisodeBatches, with one
episode per batch.
| split | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def to_list(self):
"""Convert the batch into a list of dictionaries.
Returns:
list[dict[str, np.ndarray or dict[str, np.ndarray]]]: Keys:
* observations (np.ndarray): Non-flattened array of
observations. Has shape (T, S^*) (the unflattened state
... | Convert the batch into a list of dictionaries.
Returns:
list[dict[str, np.ndarray or dict[str, np.ndarray]]]: Keys:
* observations (np.ndarray): Non-flattened array of
observations. Has shape (T, S^*) (the unflattened state
space of the curren... | to_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def from_list(cls, env_spec, paths):
"""Create a EpisodeBatch from a list of episodes.
Args:
env_spec (EnvSpec): Specification for the environment from which
this data was sampled.
paths (list[dict[str, np.ndarray or dict[str, np.ndarray]]]): Keys:
... | Create a EpisodeBatch from a list of episodes.
Args:
env_spec (EnvSpec): Specification for the environment from which
this data was sampled.
paths (list[dict[str, np.ndarray or dict[str, np.ndarray]]]): Keys:
* episode_infos (dict[str, np.ndarray]): Dicti... | from_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def next_observations(self):
r"""Get the observations seen after actions are performed.
In an :class:`~EpisodeBatch`, next_observations don't need to be stored
explicitly, since the next observation is already stored in
the batch.
Returns:
np.ndarray: The "next_obse... | Get the observations seen after actions are performed.
In an :class:`~EpisodeBatch`, next_observations don't need to be stored
explicitly, since the next observation is already stored in
the batch.
Returns:
np.ndarray: The "next_observations" with shape
:mat... | next_observations | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def episode_infos(self):
r"""Get the episode_infos.
In an :class:`~EpisodeBatch`, episode_infos only need to be stored once
per episode. However, the episode_infos field of
:class:`~TimeStepBatch` has shape :math:`(N \bullet [T])`. This method
expands episode_infos_by_episode (w... | Get the episode_infos.
In an :class:`~EpisodeBatch`, episode_infos only need to be stored once
per episode. However, the episode_infos field of
:class:`~TimeStepBatch` has shape :math:`(N \bullet [T])`. This method
expands episode_infos_by_episode (which have shape :math:`(N)`) to
... | episode_infos | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def observations_list(self):
"""Split observations into a list.
Returns:
list[np.ndarray]: Splitted list.
"""
obs_list = []
for start, stop in self._episode_ranges():
obs_list.append(self.observations[start:stop])
return obs_list | Split observations into a list.
Returns:
list[np.ndarray]: Splitted list.
| observations_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def actions_list(self):
"""Split actions into a list.
Returns:
list[np.ndarray]: Splitted list.
"""
acts_list = []
for start, stop in self._episode_ranges():
acts_list.append(self.actions[start:stop])
return acts_list | Split actions into a list.
Returns:
list[np.ndarray]: Splitted list.
| actions_list | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def padded_agent_infos(self):
"""Padded agent infos.
Returns:
dict[str, np.ndarray]: Padded agent infos. Each value must have
shape with :math:`(N, max_episode_length)` or
:math:`(N, max_episode_length, S^*)`.
"""
return {
k: pad_... | Padded agent infos.
Returns:
dict[str, np.ndarray]: Padded agent infos. Each value must have
shape with :math:`(N, max_episode_length)` or
:math:`(N, max_episode_length, S^*)`.
| padded_agent_infos | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def padded_env_infos(self):
"""Padded env infos.
Returns:
dict[str, np.ndarray]: Padded env infos. Each value must have
shape with :math:`(N, max_episode_length)` or
:math:`(N, max_episode_length, S^*)`.
"""
return {
k: pad_batch_... | Padded env infos.
Returns:
dict[str, np.ndarray]: Padded env infos. Each value must have
shape with :math:`(N, max_episode_length)` or
:math:`(N, max_episode_length, S^*)`.
| padded_env_infos | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def _space_soft_contains(space, element):
"""Check that a space has the same dimensionality as an element.
If the space's dimensionality is not available, check that the space
contains the element.
Args:
space (akro.Space or gym.Space): Space to check
element (object): Element to check... | Check that a space has the same dimensionality as an element.
If the space's dimensionality is not available, check that the space
contains the element.
Args:
space (akro.Space or gym.Space): Space to check
element (object): Element to check in space.
Returns:
bool: True iff t... | _space_soft_contains | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def check_timestep_batch(batch, array_type, ignored_fields=()):
"""Check a TimeStepBatch of any array type that has .shape.
Args:
batch (TimeStepBatch): Batch of timesteps.
array_type (type): Array type.
ignored_fields (set[str]): Set of fields to ignore checking on.
Raises:
... | Check a TimeStepBatch of any array type that has .shape.
Args:
batch (TimeStepBatch): Batch of timesteps.
array_type (type): Array type.
ignored_fields (set[str]): Set of fields to ignore checking on.
Raises:
ValueError: If an invariant of TimeStepBatch is broken.
| check_timestep_batch | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
def render_modes(self):
"""list: A list of string representing the supported render modes.
See render() for a list of modes.
""" | list: A list of string representing the supported render modes.
See render() for a list of modes.
| render_modes | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def step(self, action):
"""Steps the environment with the action and returns a `EnvStep`.
If the environment returned the last `EnvStep` of a sequence (either
of type TERMINAL or TIMEOUT) at the previous step, this call to
`step()` will start a new sequence and `action` will be ignored.... | Steps the environment with the action and returns a `EnvStep`.
If the environment returned the last `EnvStep` of a sequence (either
of type TERMINAL or TIMEOUT) at the previous step, this call to
`step()` will start a new sequence and `action` will be ignored.
If `spec.max_episode_leng... | step | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def render(self, mode):
"""Renders the environment.
The set of supported modes varies per environment. By convention,
if mode is:
* rgb_array: Return an `numpy.ndarray` with shape (x, y, 3) and type
uint8, representing RGB values for an x-by-y pixel image, suitable
... | Renders the environment.
The set of supported modes varies per environment. By convention,
if mode is:
* rgb_array: Return an `numpy.ndarray` with shape (x, y, 3) and type
uint8, representing RGB values for an x-by-y pixel image, suitable
for turning into a video.
... | render | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def visualize(self):
"""Creates a visualization of the environment.
This function should be called **only once** after `reset()` to set up
the visualization display. The visualization should be updated
when the environment is changed (i.e. when `step()` is called.)
Calling `clo... | Creates a visualization of the environment.
This function should be called **only once** after `reset()` to set up
the visualization display. The visualization should be updated
when the environment is changed (i.e. when `step()` is called.)
Calling `close()` will deallocate any resour... | visualize | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def close(self):
"""Closes the environment.
This method should close all windows invoked by `visualize()`.
Override this function in your subclass to perform any necessary
cleanup.
Environments will automatically `close()` themselves when they are
garbage collected or ... | Closes the environment.
This method should close all windows invoked by `visualize()`.
Override this function in your subclass to perform any necessary
cleanup.
Environments will automatically `close()` themselves when they are
garbage collected or when the program exits.
... | close | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def __getattr__(self, name):
"""Forward getattr request to wrapped environment.
Args:
name (str): attr (str): attribute name
Returns:
object: the wrapped attribute.
Raises:
AttributeError: if the requested attribute is a private attribute,
... | Forward getattr request to wrapped environment.
Args:
name (str): attr (str): attribute name
Returns:
object: the wrapped attribute.
Raises:
AttributeError: if the requested attribute is a private attribute,
or if the requested attribute is not... | __getattr__ | python | rlworkgroup/garage | src/garage/_environment.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_environment.py | MIT |
def make_optimizer(optimizer_type, module=None, **kwargs):
"""Create an optimizer for pyTorch & tensorflow algos.
Args:
optimizer_type (Union[type, tuple[type, dict]]): Type of optimizer.
This can be an optimizer type such as 'torch.optim.Adam' or a
tuple of type and dictionary,... | Create an optimizer for pyTorch & tensorflow algos.
Args:
optimizer_type (Union[type, tuple[type, dict]]): Type of optimizer.
This can be an optimizer type such as 'torch.optim.Adam' or a
tuple of type and dictionary, where dictionary contains arguments
to initialize the... | make_optimizer | python | rlworkgroup/garage | src/garage/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_functions.py | MIT |
def rollout(env,
agent,
*,
max_episode_length=np.inf,
animated=False,
pause_per_frame=None,
deterministic=False):
"""Sample a single episode of the agent in the environment.
Args:
agent (Policy): Policy used to select actions.
... | Sample a single episode of the agent in the environment.
Args:
agent (Policy): Policy used to select actions.
env (Environment): Environment to perform actions in.
max_episode_length (int): If the episode reaches this many timesteps,
it is truncated.
animated (bool): If ... | rollout | python | rlworkgroup/garage | src/garage/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_functions.py | MIT |
def obtain_evaluation_episodes(policy,
env,
max_episode_length=1000,
num_eps=100,
deterministic=True):
"""Sample the policy for num_eps episodes and return average values.
Args:
p... | Sample the policy for num_eps episodes and return average values.
Args:
policy (Policy): Policy to use as the actor when gathering samples.
env (Environment): The environement used to obtain episodes.
max_episode_length (int): Maximum episode length. The episode will
truncated w... | obtain_evaluation_episodes | python | rlworkgroup/garage | src/garage/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_functions.py | MIT |
def log_multitask_performance(itr, batch, discount, name_map=None):
r"""Log performance of episodes from multiple tasks.
Args:
itr (int): Iteration number to be logged.
batch (EpisodeBatch): Batch of episodes. The episodes should have
either the "task_name" or "task_id" `env_infos`.... | Log performance of episodes from multiple tasks.
Args:
itr (int): Iteration number to be logged.
batch (EpisodeBatch): Batch of episodes. The episodes should have
either the "task_name" or "task_id" `env_infos`. If the "task_name"
is not present, then `name_map` is required,... | log_multitask_performance | python | rlworkgroup/garage | src/garage/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_functions.py | MIT |
def log_performance(itr, batch, discount, prefix='Evaluation'):
"""Evaluate the performance of an algorithm on a batch of episodes.
Args:
itr (int): Iteration number.
batch (EpisodeBatch): The episodes to evaluate with.
discount (float): Discount value, from algorithm's property.
... | Evaluate the performance of an algorithm on a batch of episodes.
Args:
itr (int): Iteration number.
batch (EpisodeBatch): The episodes to evaluate with.
discount (float): Discount value, from algorithm's property.
prefix (str): Prefix to add to all logged keys.
Returns:
... | log_performance | python | rlworkgroup/garage | src/garage/_functions.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_functions.py | MIT |
def __init__(self, desc='4x4', max_episode_length=None):
"""Initialize the environment.
Args:
desc (str): grid configuration key.
max_episode_length (int): The maximum steps allowed for an episode.
"""
if isinstance(desc, str):
desc = MAPS[desc]
... | Initialize the environment.
Args:
desc (str): grid configuration key.
max_episode_length (int): The maximum steps allowed for an episode.
| __init__ | python | rlworkgroup/garage | src/garage/envs/grid_world_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/grid_world_env.py | MIT |
def step(self, action):
"""Steps the environment.
action map:
0: left
1: down
2: right
3: up
Args:
action (int): an int encoding the action
Returns:
EnvStep: The environment step resulting from the action.
Raises:
... | Steps the environment.
action map:
0: left
1: down
2: right
3: up
Args:
action (int): an int encoding the action
Returns:
EnvStep: The environment step resulting from the action.
Raises:
RuntimeError: if `step()` is ... | step | python | rlworkgroup/garage | src/garage/envs/grid_world_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/grid_world_env.py | MIT |
def render(self, mode):
"""Renders the environment.
Args:
mode (str): the mode to render with. The string must be present in
`Environment.render_modes`.
""" | Renders the environment.
Args:
mode (str): the mode to render with. The string must be present in
`Environment.render_modes`.
| render | python | rlworkgroup/garage | src/garage/envs/grid_world_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/grid_world_env.py | MIT |
def _get_possible_next_states(self, state, action):
"""Return possible next states and their probabilities.
Only next states with nonzero probabilities will be returned.
Args:
state (list): start state
action (int): action
Returns:
list: a list of p... | Return possible next states and their probabilities.
Only next states with nonzero probabilities will be returned.
Args:
state (list): start state
action (int): action
Returns:
list: a list of pairs (s', p(s'|s,a))
| _get_possible_next_states | python | rlworkgroup/garage | src/garage/envs/grid_world_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/grid_world_env.py | MIT |
def __new__(cls, *args, **kwargs):
"""Returns environment specific wrapper based on input environment type.
Args:
*args: Positional arguments
**kwargs: Keyword arguments
Returns:
garage.envs.bullet.BulletEnv: if the environment is a bullet-based
... | Returns environment specific wrapper based on input environment type.
Args:
*args: Positional arguments
**kwargs: Keyword arguments
Returns:
garage.envs.bullet.BulletEnv: if the environment is a bullet-based
environment. Else returns a garage.envs.G... | __new__ | python | rlworkgroup/garage | src/garage/envs/gym_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/gym_env.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/envs/gym_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/gym_env.py | MIT |
def _close_viewer_window(self):
"""Close viewer window.
Unfortunately, some gym environments don't close the viewer windows
properly, which leads to "out of memory" issues when several of
these environments are tested one after the other.
This method searches for the viewer obje... | Close viewer window.
Unfortunately, some gym environments don't close the viewer windows
properly, which leads to "out of memory" issues when several of
these environments are tested one after the other.
This method searches for the viewer object of type MjViewer, Viewer
or Simp... | _close_viewer_window | python | rlworkgroup/garage | src/garage/envs/gym_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/gym_env.py | MIT |
def __getattr__(self, name):
"""Handle function calls wrapped environment.
Args:
name (str): attribute name
Returns:
object: the wrapped attribute.
Raises:
AttributeError: if the requested attribute is a private
attribute, or if the requ... | Handle function calls wrapped environment.
Args:
name (str): attribute name
Returns:
object: the wrapped attribute.
Raises:
AttributeError: if the requested attribute is a private
attribute, or if the requested attribute is not found in the
... | __getattr__ | python | rlworkgroup/garage | src/garage/envs/gym_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/gym_env.py | MIT |
def sample_tasks(self, n_tasks):
"""Samples n_tasks tasks.
Part of the set_task environment protocol. To call this method, a
benchmark must have been passed in at environment construction.
Args:
n_tasks (int): Number of tasks to sample.
Returns:
dict[st... | Samples n_tasks tasks.
Part of the set_task environment protocol. To call this method, a
benchmark must have been passed in at environment construction.
Args:
n_tasks (int): Number of tasks to sample.
Returns:
dict[str,object]: Task object to pass back to `set_... | sample_tasks | python | rlworkgroup/garage | src/garage/envs/metaworld_set_task_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/metaworld_set_task_env.py | MIT |
def set_task(self, task):
"""Set the task.
Part of the set_task environment protocol.
Args:
task (dict[str,object]): Task object from `sample_tasks`.
"""
# Mixing train and test is probably a mistake
assert self._kind is None or self._kind == task['kind']
... | Set the task.
Part of the set_task environment protocol.
Args:
task (dict[str,object]): Task object from `sample_tasks`.
| set_task | python | rlworkgroup/garage | src/garage/envs/metaworld_set_task_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/metaworld_set_task_env.py | MIT |
def _fill_tasks(self):
"""Fill out _tasks after the benchmark is set.
Raises:
ValueError: If kind is not set to "train" or "test"
"""
if self._add_env_onehot:
if (self._kind == 'test'
or 'metaworld.ML' in repr(type(self._benchmark))):
... | Fill out _tasks after the benchmark is set.
Raises:
ValueError: If kind is not set to "train" or "test"
| _fill_tasks | python | rlworkgroup/garage | src/garage/envs/metaworld_set_task_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/metaworld_set_task_env.py | MIT |
def round_robin_strategy(num_tasks, last_task=None):
"""A function for sampling tasks in round robin fashion.
Args:
num_tasks (int): Total number of tasks.
last_task (int): Previously sampled task.
Returns:
int: task id.
"""
if last_task is None:
return 0
retu... | A function for sampling tasks in round robin fashion.
Args:
num_tasks (int): Total number of tasks.
last_task (int): Previously sampled task.
Returns:
int: task id.
| round_robin_strategy | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.py | MIT |
def observation_space(self):
"""Observation space.
Returns:
akro.Box: Observation space.
"""
if self._mode == 'vanilla':
return self._env.observation_space
elif self._mode == 'add-onehot':
task_lb, task_ub = self.task_space.bounds
... | Observation space.
Returns:
akro.Box: Observation space.
| observation_space | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.py | MIT |
def task_space(self):
"""Task Space.
Returns:
akro.Box: Task space.
"""
one_hot_ub = np.ones(self.num_tasks)
one_hot_lb = np.zeros(self.num_tasks)
return akro.Box(one_hot_lb, one_hot_ub) | Task Space.
Returns:
akro.Box: Task space.
| task_space | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.py | MIT |
def active_task_index(self):
"""Index of active task env.
Returns:
int: Index of active task.
"""
if hasattr(self._env, 'active_task_index'):
return self._env.active_task_index
else:
return self._active_task_index | Index of active task env.
Returns:
int: Index of active task.
| active_task_index | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.py | MIT |
def step(self, action):
"""Step the active task env.
Args:
action (object): object to be passed in Environment.reset(action)
Returns:
EnvStep: The environment step resulting from the action.
"""
es = self._env.step(action)
if self._mode == 'add... | Step the active task env.
Args:
action (object): object to be passed in Environment.reset(action)
Returns:
EnvStep: The environment step resulting from the action.
| step | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.py | MIT |
def _active_task_one_hot(self):
"""One-hot representation of active task.
Returns:
numpy.ndarray: one-hot representation of active task
"""
one_hot = np.zeros(self.task_space.shape)
index = self.active_task_index or 0
one_hot[index] = self.task_space.high[in... | One-hot representation of active task.
Returns:
numpy.ndarray: one-hot representation of active task
| _active_task_one_hot | python | rlworkgroup/garage | src/garage/envs/multi_env_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/multi_env_wrapper.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/envs/normalized_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/normalized_env.py | MIT |
def _apply_normalize_obs(self, obs):
"""Compute normalized observation.
Args:
obs (np.ndarray): Observation.
Returns:
np.ndarray: Normalized observation.
"""
self._update_obs_estimate(obs)
flat_obs = self._env.observation_space.flatten(obs)
... | Compute normalized observation.
Args:
obs (np.ndarray): Observation.
Returns:
np.ndarray: Normalized observation.
| _apply_normalize_obs | python | rlworkgroup/garage | src/garage/envs/normalized_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/normalized_env.py | MIT |
def _apply_normalize_reward(self, reward):
"""Compute normalized reward.
Args:
reward (float): Reward.
Returns:
float: Normalized reward.
"""
self._update_reward_estimate(reward)
return reward / (np.sqrt(self._reward_var) + 1e-8) | Compute normalized reward.
Args:
reward (float): Reward.
Returns:
float: Normalized reward.
| _apply_normalize_reward | python | rlworkgroup/garage | src/garage/envs/normalized_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/normalized_env.py | MIT |
def step(self, action):
"""Step the environment.
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
... | Step the environment.
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
constr... | step | python | rlworkgroup/garage | src/garage/envs/point_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/point_env.py | MIT |
def sample_tasks(self, num_tasks):
"""Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, np.ndarray]]: A list of "tasks", where each task is
a dictionary containing a single key, "goal", mapping ... | Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, np.ndarray]]: A list of "tasks", where each task is
a dictionary containing a single key, "goal", mapping to a
point in 2D space.
... | sample_tasks | python | rlworkgroup/garage | src/garage/envs/point_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/point_env.py | MIT |
def set_task(self, task):
"""Reset with a task.
Args:
task (dict[str, np.ndarray]): A task (a dictionary containing a
single key, "goal", which should be a point in 2D space).
"""
self._task = task
self._goal = task['goal'] | Reset with a task.
Args:
task (dict[str, np.ndarray]): A task (a dictionary containing a
single key, "goal", which should be a point in 2D space).
| set_task | python | rlworkgroup/garage | src/garage/envs/point_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/point_env.py | MIT |
def step(self, action):
"""gym.Env step for the active task env.
Args:
action (np.ndarray): Action performed by the agent in the
environment.
Returns:
tuple:
np.ndarray: Agent's observation of the current environment.
floa... | gym.Env step for the active task env.
Args:
action (np.ndarray): Action performed by the agent in the
environment.
Returns:
tuple:
np.ndarray: Agent's observation of the current environment.
float: Amount of reward yielded by prev... | step | python | rlworkgroup/garage | src/garage/envs/task_name_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/task_name_wrapper.py | MIT |
def step(self, action):
"""Environment step for the active task env.
Args:
action (np.ndarray): Action performed by the agent in the
environment.
Returns:
EnvStep: The environment step resulting from the action.
"""
es = self._env.step(a... | Environment step for the active task env.
Args:
action (np.ndarray): Action performed by the agent in the
environment.
Returns:
EnvStep: The environment step resulting from the action.
| step | python | rlworkgroup/garage | src/garage/envs/task_onehot_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/task_onehot_wrapper.py | MIT |
def _obs_with_one_hot(self, obs):
"""Concatenate observation and task one-hot.
Args:
obs (numpy.ndarray): observation
Returns:
numpy.ndarray: observation + task one-hot.
"""
one_hot = np.zeros(self._n_total_tasks)
one_hot[self._task_index] = 1.0... | Concatenate observation and task one-hot.
Args:
obs (numpy.ndarray): observation
Returns:
numpy.ndarray: observation + task one-hot.
| _obs_with_one_hot | python | rlworkgroup/garage | src/garage/envs/task_onehot_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/task_onehot_wrapper.py | MIT |
def wrap_env_list(cls, envs):
"""Wrap a list of environments, giving each environment a one-hot.
This is the primary way of constructing instances of this class.
It's mostly useful when training multi-task algorithms using a
multi-task aware sampler.
For example:
'''
... | Wrap a list of environments, giving each environment a one-hot.
This is the primary way of constructing instances of this class.
It's mostly useful when training multi-task algorithms using a
multi-task aware sampler.
For example:
'''
.. code-block:: python
... | wrap_env_list | python | rlworkgroup/garage | src/garage/envs/task_onehot_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/task_onehot_wrapper.py | MIT |
def wrap_env_cons_list(cls, env_cons):
"""Wrap a list of environment constructors, giving each a one-hot.
This function is useful if you want to avoid constructing any
environments in the main experiment process, and are using a multi-task
aware remote sampler (i.e. `~RaySampler`).
... | Wrap a list of environment constructors, giving each a one-hot.
This function is useful if you want to avoid constructing any
environments in the main experiment process, and are using a multi-task
aware remote sampler (i.e. `~RaySampler`).
For example:
'''
.. code-bloc... | wrap_env_cons_list | python | rlworkgroup/garage | src/garage/envs/task_onehot_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/task_onehot_wrapper.py | MIT |
def __init__(self, env, name=None):
"""Create a DMControlEnv.
Args:
env (dm_control.suite.Task): The wrapped dm_control environment.
name (str): Name of the environment.
"""
self._env = env
self._name = name or type(env.task).__name__
self._viewe... | Create a DMControlEnv.
Args:
env (dm_control.suite.Task): The wrapped dm_control environment.
name (str): Name of the environment.
| __init__ | python | rlworkgroup/garage | src/garage/envs/dm_control/dm_control_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/dm_control/dm_control_env.py | MIT |
def step(self, action):
"""Steps the environment with the action and returns a `EnvStep`.
Args:
action (object): input action
Returns:
EnvStep: The environment step resulting from the action.
Raises:
RuntimeError: if `step()` is called after the env... | Steps the environment with the action and returns a `EnvStep`.
Args:
action (object): input action
Returns:
EnvStep: The environment step resulting from the action.
Raises:
RuntimeError: if `step()` is called after the environment has been
c... | step | python | rlworkgroup/garage | src/garage/envs/dm_control/dm_control_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/dm_control/dm_control_env.py | MIT |
def render(self, mode):
"""Render the environment.
Args:
mode (str): render mode.
Returns:
np.ndarray: if mode is 'rgb_array', else return None.
Raises:
ValueError: if mode is not supported.
"""
self._validate_render_mode(mode)
... | Render the environment.
Args:
mode (str): render mode.
Returns:
np.ndarray: if mode is 'rgb_array', else return None.
Raises:
ValueError: if mode is not supported.
| render | python | rlworkgroup/garage | src/garage/envs/dm_control/dm_control_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/dm_control/dm_control_env.py | MIT |
def __getstate__(self):
"""See `Object.__getstate__`.
Returns:
dict: dict of the class.
"""
d = self.__dict__.copy()
d['_viewer'] = None
return d | See `Object.__getstate__`.
Returns:
dict: dict of the class.
| __getstate__ | python | rlworkgroup/garage | src/garage/envs/dm_control/dm_control_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/dm_control/dm_control_env.py | MIT |
def step(self, action):
"""Take one step in the environment.
Equivalent to step in HalfCheetahEnv, but with different rewards.
Args:
action (np.ndarray): The action to take in the environment.
Raises:
ValueError: If the current direction is not 1.0 or -1.0.
... | Take one step in the environment.
Equivalent to step in HalfCheetahEnv, but with different rewards.
Args:
action (np.ndarray): The action to take in the environment.
Raises:
ValueError: If the current direction is not 1.0 or -1.0.
Returns:
tuple:
... | step | python | rlworkgroup/garage | src/garage/envs/mujoco/half_cheetah_dir_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/mujoco/half_cheetah_dir_env.py | MIT |
def sample_tasks(self, num_tasks):
"""Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, float]]: A list of "tasks," where each task is a
dictionary containing a single key, "direction", mapping ... | Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, float]]: A list of "tasks," where each task is a
dictionary containing a single key, "direction", mapping to -1
or 1.
| sample_tasks | python | rlworkgroup/garage | src/garage/envs/mujoco/half_cheetah_dir_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/mujoco/half_cheetah_dir_env.py | MIT |
def _get_obs(self):
"""Get a low-dimensional observation of the state.
Returns:
np.ndarray: Contains the flattened angle quaternion, angular
velocity quaternion, and cartesian position.
"""
return np.concatenate([
self.sim.data.qpos.flat[1:],
... | Get a low-dimensional observation of the state.
Returns:
np.ndarray: Contains the flattened angle quaternion, angular
velocity quaternion, and cartesian position.
| _get_obs | python | rlworkgroup/garage | src/garage/envs/mujoco/half_cheetah_env_meta_base.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/mujoco/half_cheetah_env_meta_base.py | MIT |
def step(self, action):
"""Take one step in the environment.
Equivalent to step in HalfCheetahEnv, but with different rewards.
Args:
action (np.ndarray): The action to take in the environment.
Returns:
tuple:
* observation (np.ndarray): The obse... | Take one step in the environment.
Equivalent to step in HalfCheetahEnv, but with different rewards.
Args:
action (np.ndarray): The action to take in the environment.
Returns:
tuple:
* observation (np.ndarray): The observation of the environment.
... | step | python | rlworkgroup/garage | src/garage/envs/mujoco/half_cheetah_vel_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/mujoco/half_cheetah_vel_env.py | MIT |
def sample_tasks(self, num_tasks):
"""Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, float]]: A list of "tasks," where each task is a
dictionary containing a single key, "velocity", mapping t... | Sample a list of `num_tasks` tasks.
Args:
num_tasks (int): Number of tasks to sample.
Returns:
list[dict[str, float]]: A list of "tasks," where each task is a
dictionary containing a single key, "velocity", mapping to a
value between 0 and 2.
... | sample_tasks | python | rlworkgroup/garage | src/garage/envs/mujoco/half_cheetah_vel_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/mujoco/half_cheetah_vel_env.py | MIT |
def reset(self, **kwargs):
"""
gym.Env reset function.
Reset only when lives are lost.
"""
if self._was_real_done:
obs = self.env.reset(**kwargs)
else:
# no-op step
obs, _, _, _ = self.env.step(0)
self._lives = self.env.unwrapp... |
gym.Env reset function.
Reset only when lives are lost.
| reset | python | rlworkgroup/garage | src/garage/envs/wrappers/episodic_life.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/episodic_life.py | MIT |
def reset(self, **kwargs):
"""gym.Env reset function.
Args:
kwargs (dict): extra arguments passed to gym.Env.reset()
Returns:
np.ndarray: next observation.
"""
self.env.reset(**kwargs)
obs, _, done, _ = self.env.step(1)
if done:
... | gym.Env reset function.
Args:
kwargs (dict): extra arguments passed to gym.Env.reset()
Returns:
np.ndarray: next observation.
| reset | python | rlworkgroup/garage | src/garage/envs/wrappers/fire_reset.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/fire_reset.py | MIT |
def step(self, action):
"""See gym.Env.
Args:
action (np.ndarray): Action conforming to action_space
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra inf... | See gym.Env.
Args:
action (np.ndarray): Action conforming to action_space
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra information from the environment.
... | step | python | rlworkgroup/garage | src/garage/envs/wrappers/grayscale.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/grayscale.py | MIT |
def _color_to_grayscale(obs):
"""Convert a 3-channel color observation image to grayscale and uint8.
Args:
obs (np.ndarray): Observation array, conforming to observation_space
Returns:
np.ndarray: 1-channel grayscale version of obs, represented as uint8
"""
with warnings.catch_warni... | Convert a 3-channel color observation image to grayscale and uint8.
Args:
obs (np.ndarray): Observation array, conforming to observation_space
Returns:
np.ndarray: 1-channel grayscale version of obs, represented as uint8
| _color_to_grayscale | python | rlworkgroup/garage | src/garage/envs/wrappers/grayscale.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/grayscale.py | MIT |
def step(self, action):
"""Repeat action, sum reward, and max over last two observations.
Args:
action (int): action to take in the atari environment.
Returns:
np.ndarray: observation of shape :math:`(O*,)` representating
the max values over the last two... | Repeat action, sum reward, and max over last two observations.
Args:
action (int): action to take in the atari environment.
Returns:
np.ndarray: observation of shape :math:`(O*,)` representating
the max values over the last two oservations.
float: Re... | step | python | rlworkgroup/garage | src/garage/envs/wrappers/max_and_skip.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/max_and_skip.py | MIT |
def step(self, action):
"""gym.Env step function.
Performs one action step in the enviornment.
Args:
action (np.ndarray): Action of shape :math:`(A*, )`
to pass to the environment.
Returns:
np.ndarray: Pixel observation of shape :math:`(O*, )`
... | gym.Env step function.
Performs one action step in the enviornment.
Args:
action (np.ndarray): Action of shape :math:`(A*, )`
to pass to the environment.
Returns:
np.ndarray: Pixel observation of shape :math:`(O*, )`
from the wrapped env... | step | python | rlworkgroup/garage | src/garage/envs/wrappers/pixel_observation.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/pixel_observation.py | MIT |
def reset(self):
"""gym.Env reset function.
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra information from the environment.
"""
observation = self.env.rese... | gym.Env reset function.
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra information from the environment.
| reset | python | rlworkgroup/garage | src/garage/envs/wrappers/stack_frames.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/stack_frames.py | MIT |
def step(self, action):
"""gym.Env step function.
Args:
action (int): index of the action to take.
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra infor... | gym.Env step function.
Args:
action (int): index of the action to take.
Returns:
np.ndarray: Observation conforming to observation_space
float: Reward for this step
bool: Termination signal
dict: Extra information from the environment.
... | step | python | rlworkgroup/garage | src/garage/envs/wrappers/stack_frames.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/envs/wrappers/stack_frames.py | MIT |
def query_yes_no(question, default='yes'):
"""Ask a yes/no question via raw_input() and return their answer.
Args:
question (str): Printed to user.
default (str or None): Default if user just hits enter.
Raises:
ValueError: If the provided default is invalid.
Returns:
... | Ask a yes/no question via raw_input() and return their answer.
Args:
question (str): Printed to user.
default (str or None): Default if user just hits enter.
Raises:
ValueError: If the provided default is invalid.
Returns:
bool: True for "yes"y answers, False for "no".
... | query_yes_no | python | rlworkgroup/garage | src/garage/examples/sim_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/sim_policy.py | MIT |
def step_bullet_kuka_env(n_steps=1000):
"""Load, step, and visualize a Bullet Kuka environment.
Args:
n_steps (int): number of steps to run.
"""
# Construct the environment
env = GymEnv(gym.make('KukaBulletEnv-v0', renders=True, isDiscrete=True))
# Reset the environment and launch the... | Load, step, and visualize a Bullet Kuka environment.
Args:
n_steps (int): number of steps to run.
| step_bullet_kuka_env | python | rlworkgroup/garage | src/garage/examples/step_bullet_kuka_env.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/step_bullet_kuka_env.py | MIT |
def cem_cartpole(ctxt=None, seed=1):
"""Train CEM with Cartpole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
deter... | Train CEM with Cartpole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| cem_cartpole | python | rlworkgroup/garage | src/garage/examples/np/cem_cartpole.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/cem_cartpole.py | MIT |
def cma_es_cartpole(ctxt=None, seed=1):
"""Train CMA_ES with Cartpole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train CMA_ES with Cartpole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| cma_es_cartpole | python | rlworkgroup/garage | src/garage/examples/np/cma_es_cartpole.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/cma_es_cartpole.py | MIT |
def train(self, trainer):
"""Get samples and train the policy.
Args:
trainer (Trainer): Trainer.
"""
for epoch in trainer.step_epochs():
samples = trainer.obtain_samples(epoch)
log_performance(epoch,
EpisodeBatch.from_list... | Get samples and train the policy.
Args:
trainer (Trainer): Trainer.
| train | python | rlworkgroup/garage | src/garage/examples/np/tutorial_cem.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/tutorial_cem.py | MIT |
def _train_once(self, epoch, paths):
"""Perform one step of policy optimization given one batch of samples.
Args:
epoch (int): Iteration number.
paths (list[dict]): A list of collected paths.
Returns:
float: The average return of epoch cycle.
"""
... | Perform one step of policy optimization given one batch of samples.
Args:
epoch (int): Iteration number.
paths (list[dict]): A list of collected paths.
Returns:
float: The average return of epoch cycle.
| _train_once | python | rlworkgroup/garage | src/garage/examples/np/tutorial_cem.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/tutorial_cem.py | MIT |
def _sample_params(self, epoch):
"""Return sample parameters.
Args:
epoch (int): Epoch number.
Returns:
np.ndarray: A numpy array of parameter values.
"""
extra_var_mult = max(1.0 - epoch / self._extra_decay_time, 0)
sample_std = np.sqrt(
... | Return sample parameters.
Args:
epoch (int): Epoch number.
Returns:
np.ndarray: A numpy array of parameter values.
| _sample_params | python | rlworkgroup/garage | src/garage/examples/np/tutorial_cem.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/tutorial_cem.py | MIT |
def tutorial_cem(ctxt=None):
"""Train CEM with Cartpole-v1 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
"""
set_seed(100)
with TFTrainer(ctxt) as trainer:
env = GymEnv('CartPole-... | Train CEM with Cartpole-v1 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
| tutorial_cem | python | rlworkgroup/garage | src/garage/examples/np/tutorial_cem.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/np/tutorial_cem.py | MIT |
def ddpg_pendulum(ctxt=None, seed=1):
"""Train DDPG with InvertedDoublePendulum-v2 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train DDPG with InvertedDoublePendulum-v2 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| ddpg_pendulum | python | rlworkgroup/garage | src/garage/examples/tf/ddpg_pendulum.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/ddpg_pendulum.py | MIT |
def dqn_cartpole(ctxt=None, seed=1):
"""Train TRPO with CubeCrash-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
det... | Train TRPO with CubeCrash-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| dqn_cartpole | python | rlworkgroup/garage | src/garage/examples/tf/dqn_cartpole.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/dqn_cartpole.py | MIT |
def dqn_pong(ctxt=None, seed=1, buffer_size=int(5e4), max_episode_length=500):
"""Train DQN on PongNoFrameskip-v4 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the rando... | Train DQN on PongNoFrameskip-v4 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
buffer_size (int): Numb... | dqn_pong | python | rlworkgroup/garage | src/garage/examples/tf/dqn_pong.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/dqn_pong.py | MIT |
def erwr_cartpole(ctxt=None, seed=1):
"""Train with ERWR on CartPole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train with ERWR on CartPole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| erwr_cartpole | python | rlworkgroup/garage | src/garage/examples/tf/erwr_cartpole.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/erwr_cartpole.py | MIT |
def her_ddpg_fetchreach(ctxt=None, seed=1):
"""Train DDPG + HER on the goal-conditioned FetchReach env.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to p... | Train DDPG + HER on the goal-conditioned FetchReach env.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| her_ddpg_fetchreach | python | rlworkgroup/garage | src/garage/examples/tf/her_ddpg_fetchreach.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/her_ddpg_fetchreach.py | MIT |
def multi_env_ppo(ctxt=None, seed=1):
"""Train PPO on two Atari environments simultaneously.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train PPO on two Atari environments simultaneously.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| multi_env_ppo | python | rlworkgroup/garage | src/garage/examples/tf/multi_env_ppo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/multi_env_ppo.py | MIT |
def multi_env_trpo(ctxt=None, seed=1):
"""Train TRPO on two different PointEnv instances.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train TRPO on two different PointEnv instances.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| multi_env_trpo | python | rlworkgroup/garage | src/garage/examples/tf/multi_env_trpo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/multi_env_trpo.py | MIT |
def ppo_memorize_digits(ctxt=None,
seed=1,
batch_size=4000,
max_episode_length=100):
"""Train PPO on MemorizeDigits-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by ... | Train PPO on MemorizeDigits-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Number... | ppo_memorize_digits | python | rlworkgroup/garage | src/garage/examples/tf/ppo_memorize_digits.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/ppo_memorize_digits.py | MIT |
def ppo_pendulum(ctxt=None, seed=1):
"""Train PPO with InvertedDoublePendulum-v2 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train PPO with InvertedDoublePendulum-v2 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| ppo_pendulum | python | rlworkgroup/garage | src/garage/examples/tf/ppo_pendulum.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/ppo_pendulum.py | MIT |
def reps_gym_cartpole(ctxt=None, seed=1):
"""Train REPS with CartPole-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Train REPS with CartPole-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| reps_gym_cartpole | python | rlworkgroup/garage | src/garage/examples/tf/reps_gym_cartpole.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/reps_gym_cartpole.py | MIT |
def rl2_ppo_halfcheetah(ctxt, seed, max_episode_length, meta_batch_size,
n_epochs, episode_per_task):
"""Train PPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the snapshotter.
... | Train PPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
max_episode_length (int): Maximum le... | rl2_ppo_halfcheetah | python | rlworkgroup/garage | src/garage/examples/tf/rl2_ppo_halfcheetah.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_halfcheetah.py | MIT |
def rl2_ppo_halfcheetah_meta_test(ctxt, seed, meta_batch_size, n_epochs,
episode_per_task):
"""Perform meta-testing on RL2PPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the ... | Perform meta-testing on RL2PPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
meta_... | rl2_ppo_halfcheetah_meta_test | python | rlworkgroup/garage | src/garage/examples/tf/rl2_ppo_halfcheetah_meta_test.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_halfcheetah_meta_test.py | MIT |
def rl2_ppo_metaworld_ml10(ctxt, seed, meta_batch_size, n_epochs,
episode_per_task):
"""Train RL2 PPO with ML10 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int... | Train RL2 PPO with ML10 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
meta_batch_size (int): Meta bat... | rl2_ppo_metaworld_ml10 | python | rlworkgroup/garage | src/garage/examples/tf/rl2_ppo_metaworld_ml10.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_metaworld_ml10.py | MIT |
def rl2_ppo_metaworld_ml1_push(ctxt, seed, meta_batch_size, n_epochs,
episode_per_task):
"""Train RL2 PPO with ML1 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
... | Train RL2 PPO with ML1 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
meta_batch_size (int): Meta ba... | rl2_ppo_metaworld_ml1_push | python | rlworkgroup/garage | src/garage/examples/tf/rl2_ppo_metaworld_ml1_push.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_metaworld_ml1_push.py | MIT |
def rl2_ppo_metaworld_ml45(ctxt, seed, meta_batch_size, n_epochs,
episode_per_task):
"""Train PPO with ML45 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int):... | Train PPO with ML45 environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
meta_batch_size (int): Meta batch... | rl2_ppo_metaworld_ml45 | python | rlworkgroup/garage | src/garage/examples/tf/rl2_ppo_metaworld_ml45.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_ppo_metaworld_ml45.py | MIT |
def rl2_trpo_halfcheetah(ctxt, seed, max_episode_length, meta_batch_size,
n_epochs, episode_per_task):
"""Train TRPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshott... | Train TRPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
max_episode_length (int):... | rl2_trpo_halfcheetah | python | rlworkgroup/garage | src/garage/examples/tf/rl2_trpo_halfcheetah.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/rl2_trpo_halfcheetah.py | MIT |
def td3_pendulum(ctxt=None, seed=1):
"""Wrap TD3 training task in the run_task function.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
... | Wrap TD3 training task in the run_task function.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
| td3_pendulum | python | rlworkgroup/garage | src/garage/examples/tf/td3_pendulum.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/td3_pendulum.py | MIT |
def te_ppo_mt10(ctxt, seed, n_epochs, batch_size_per_task, n_tasks):
"""Train Task Embedding PPO with PointEnv.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number gene... | Train Task Embedding PPO with PointEnv.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
seed (int): Used to seed the random number generator to produce
determinism.
n_epochs (int): Total numb... | te_ppo_mt10 | python | rlworkgroup/garage | src/garage/examples/tf/te_ppo_metaworld_mt10.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/examples/tf/te_ppo_metaworld_mt10.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.