code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
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def __init__(
self, url, poolclass=None, chunk_size=1024 ** 2 * 100,
table_postfix=''):
"""
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738. I'll create a table ``models`` to
store the pickles in if i... |
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738. I'll create a table ``models`` to
store the pickles in if it doesn't exist yet.
:param sqlalchemy.pool.Pool poolclass:
A class specifying DB connection behavior of... | __init__ | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def __init__(self,
impl,
update_cache_rrule=None,
check_version=True,
):
"""
:param ModelPersister impl:
The underlying (decorated) persister object.
:param dict update_cache_rrule:
Optional keyword argument... |
:param ModelPersister impl:
The underlying (decorated) persister object.
:param dict update_cache_rrule:
Optional keyword arguments for a
:class:`dateutil.rrule.rrule` that determines when the cache
will be updated. See :class:`~palladium.util.RruleThread` for
... | __init__ | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def make_ujson_response(obj, status_code=200):
"""Encodes the given *obj* to json and wraps it in a response.
:return:
A Flask response.
"""
json_encoded = ujson.encode(obj, ensure_ascii=False)
resp = make_response(json_encoded)
resp.mimetype = 'application/json'
resp.content_type = '... | Encodes the given *obj* to json and wraps it in a response.
:return:
A Flask response.
| make_ujson_response | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def __init__(
self,
mapping,
params=(),
entry_point='/predict',
decorator_list_name='predict_decorators',
predict_proba=False,
unwrap_sample=False,
**kwargs
):
"""
:param mapping:
A list of query parameters and their type that... |
:param mapping:
A list of query parameters and their type that should be
included in the request. These will be processed in the
:meth:`sample_from_data` method to construct a sample
that can be used for prediction. An example that expects
two request paramet... | __init__ | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def sample_from_data(self, model, data):
"""Convert incoming sample *data* into a numpy array.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the sample's data, typically retrieved from
``request.args`` or si... | Convert incoming sample *data* into a numpy array.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the sample's data, typically retrieved from
``request.args`` or similar.
| sample_from_data | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def params_from_data(self, model, data):
"""Retrieve additional parameters (keyword arguments) for
``model.predict`` from request *data*.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the parameter data, typic... | Retrieve additional parameters (keyword arguments) for
``model.predict`` from request *data*.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the parameter data, typically retrieved
from ``request.args`` or si... | params_from_data | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def response_from_prediction(self, y_pred, single=True):
"""Turns a model's prediction in *y_pred* into a JSON
response.
"""
result = y_pred.tolist()
if single:
result = result[0]
response = {
'metadata': get_metadata(),
'result': resul... | Turns a model's prediction in *y_pred* into a JSON
response.
| response_from_prediction | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def create_predict_function(
route, predict_service, decorator_list_name, config):
"""Creates a predict function and registers it to
the Flask app using the route decorator.
:param str route:
Path of the entry point.
:param palladium.interfaces.PredictService predict_service:
The p... | Creates a predict function and registers it to
the Flask app using the route decorator.
:param str route:
Path of the entry point.
:param palladium.interfaces.PredictService predict_service:
The predict service to be registered to this entry point.
:param str decorator_list_name:
Th... | create_predict_function | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def devserver_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Serve the web API for development.
Usage:
pld-devserver [options]
Options:
-h --help Show this screen.
--host=<host> The host to use [default: 0.0.0.0].
--port=<port> The port to use [default: 5000].
--de... | Serve the web API for development.
Usage:
pld-devserver [options]
Options:
-h --help Show this screen.
--host=<host> The host to use [default: 0.0.0.0].
--port=<port> The port to use [default: 5000].
--debug=<debug> Whether or not to use debug mode [default: 0].
| devserver_cmd | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def listen(self, io_in, io_out, io_err):
"""Listens to provided io stream and writes predictions
to output. In case of errors, the error stream will be used.
"""
for line in io_in:
if line.strip().lower() == 'exit':
break
try:
y_pr... | Listens to provided io stream and writes predictions
to output. In case of errors, the error stream will be used.
| listen | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def stream_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Start the streaming server, which listens to stdin, processes line
by line, and returns predictions.
The input should consist of a list of json objects, where each object
will result in a prediction. Each line is processed in a batch.
Example input (mus... | Start the streaming server, which listens to stdin, processes line
by line, and returns predictions.
The input should consist of a list of json objects, where each object
will result in a prediction. Each line is processed in a batch.
Example input (must be on a single line):
[{"sepal length": 1.0, "sepal width":... | stream_cmd | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def apply_kwargs(func, **kwargs):
"""Call *func* with kwargs, but only those kwargs that it accepts.
"""
new_kwargs = {}
params = signature(func).parameters
for param_name in params.keys():
if param_name in kwargs:
new_kwargs[param_name] = kwargs[param_name]
return func(**new... | Call *func* with kwargs, but only those kwargs that it accepts.
| apply_kwargs | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def args_from_config(func):
"""Decorator that injects parameters from the configuration.
"""
func_args = signature(func).parameters
@wraps(func)
def wrapper(*args, **kwargs):
config = get_config()
for i, argname in enumerate(func_args):
if len(args) > i or argname in kwa... | Decorator that injects parameters from the configuration.
| args_from_config | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def session_scope(session):
"""Provide a transactional scope around a series of operations."""
try:
yield session
session.commit()
except:
session.rollback()
raise
finally:
session.close() | Provide a transactional scope around a series of operations. | session_scope | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def __init__(self, func, rrule, sleep_between_checks=60):
"""
:param callable func:
The function that I will call periodically.
:param rrule rrule:
The :class:`dateutil.rrule.rrule` recurrence rule that
defines when I will do the calls. See the `python-dateutil
... |
:param callable func:
The function that I will call periodically.
:param rrule rrule:
The :class:`dateutil.rrule.rrule` recurrence rule that
defines when I will do the calls. See the `python-dateutil
docs <https://labix.org/python-dateutil>`_ for details on
... | __init__ | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def memory_usage_psutil():
"""Return the current process memory usage in MB.
"""
process = psutil.Process(os.getpid())
mem = process.memory_info()[0] / float(2 ** 20)
mem_vms = process.memory_info()[1] / float(2 ** 20)
return mem, mem_vms | Return the current process memory usage in MB.
| memory_usage_psutil | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def upgrade_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Upgrade the database to the latest version.
Usage:
pld-ugprade [options]
Options:
--from=<v> Upgrade from a specific version, overriding
the version stored in the database.
--to=<v> Upgrade... | Upgrade the database to the latest version.
Usage:
pld-ugprade [options]
Options:
--from=<v> Upgrade from a specific version, overriding
the version stored in the database.
--to=<v> Upgrade to a specific version instead of the
... | upgrade_cmd | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def export_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Export a model from one model persister to another.
The model persister to export to is supposed to be available in the
configuration file under the 'model_persister_export' key.
Usage:
pld-export [options]
Options:
--version=<v> Export a... | Export a model from one model persister to another.
The model persister to export to is supposed to be available in the
configuration file under the 'model_persister_export' key.
Usage:
pld-export [options]
Options:
--version=<v> Export a specific version rather than the active
... | export_cmd | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def Partial(func, **kwargs):
"""Allows the use of partially applied functions in the
configuration.
"""
if isinstance(func, str):
func = resolve_dotted_name(func)
partial_func = partial(func, **kwargs)
update_wrapper(partial_func, func)
return partial_func | Allows the use of partially applied functions in the
configuration.
| Partial | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def test_upload(self, mocked_requests, persister):
""" test upload of model and metadata """
model = Dummy(name='mymodel')
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.head(get_md_url, status_code=404)
put_model_body = None
def handle_put_m... | test upload of model and metadata | test_upload | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def test_download(self, mocked_requests, persister):
""" test download and activation of a model """
expected = Dummy(name='mymodel', __metadata__={})
zipped_model = gzip.compress(pickle.dumps(expected))
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.... | test download and activation of a model | test_download | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def test_delete(self, mocked_requests, persister):
""" test deleting a model and metadata update """
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.head(get_md_url, status_code=200)
mocked_requests.get(
get_md_url,
json={"models": [{"v... | test deleting a model and metadata update | test_delete | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def flask_app_test(request, config):
"""A Flask app where _url_map, _view_functions, _rules, and
_rules_by_end_point will be reset to the previous values after
running the test.
"""
from palladium.server import app
orig_rules = app.url_map._rules
app.url_map._rules = [rule for rule in app.u... | A Flask app where _url_map, _view_functions, _rules, and
_rules_by_end_point will be reset to the previous values after
running the test.
| flask_app_test | python | ottogroup/palladium | palladium/tests/__init__.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/__init__.py | Apache-2.0 |
def get_task(benchmark, env_id):
"""Get a task by env_id.
Return None if the benchmark doesn't have the env.
"""
return next(
filter(lambda task: task['env_id'] == env_id, benchmark['tasks']),
None) | Get a task by env_id.
Return None if the benchmark doesn't have the env.
| get_task | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmarks.py | MIT |
def find_task_for_env_id_in_any_benchmark(env_id):
"""Find task for env id in any benchmark."""
for bm in _BENCHMARKS:
for task in bm['tasks']:
if task['env_id'] == env_id:
return bm, task
return None, None | Find task for env id in any benchmark. | find_task_for_env_id_in_any_benchmark | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmarks.py | MIT |
def continuous_mlp_policy_tf_ddpg_benchmarks():
"""Run benchmarking experiments for Continuous MLP Policy on TF-DDPG."""
seeds = random.sample(range(100), 5)
iterate_experiments(continuous_mlp_policy, MuJoCo1M_ENV_SET, seeds=seeds) | Run benchmarking experiments for Continuous MLP Policy on TF-DDPG. | continuous_mlp_policy_tf_ddpg_benchmarks | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmark_policies.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmark_policies.py | MIT |
def benchmark(exec_func=None, *, plot=True, auto=False):
"""Decorator for benchmark function.
Args:
exec_func (func): The experiment function.
plot (bool): Whether the result of this run needs to be plotted.
PNG files will be generated in sub folder /plot.
auto (auto): Wheth... | Decorator for benchmark function.
Args:
exec_func (func): The experiment function.
plot (bool): Whether the result of this run needs to be plotted.
PNG files will be generated in sub folder /plot.
auto (auto): Whether this is automatic benchmarking. JSON files
will b... | benchmark | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def iterate_experiments(func,
env_ids,
snapshot_config=None,
seeds=None,
xcolumn='TotalEnvSteps',
xlabel='Total Environment Steps',
ycolumn='Evaluation/AverageReturn',
... | Iterate experiments for benchmarking over env_ids and seeds.
Args:
env_ids (list[str]): List of environment ids.
snapshot_config (garage.experiment.SnapshotConfig): The experiment
configuration used by :class:`~Trainer` to create the
:class:`~Snapshotter`.
seeds (lis... | iterate_experiments | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _get_log_dir(exec_func_name):
"""Get the log directory given the experiment name.
Args:
exec_func_name (str): The function name which runs benchmarks.
Returns:
str: Log directory.
"""
cwd = pathlib.Path.cwd()
return str(cwd.joinpath('data', 'local', 'benchmarks', exec_func... | Get the log directory given the experiment name.
Args:
exec_func_name (str): The function name which runs benchmarks.
Returns:
str: Log directory.
| _get_log_dir | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _read_csv(log_dir, xcolumn, ycolumn):
"""Read csv files and return xs and ys.
Args:
log_dir (str): Log directory for csv file.
xcolumn (str): Which column should be the JSON x axis.
ycolumn (str): Which column should be the JSON y axis.
Returns:
list: List of x axis poi... | Read csv files and return xs and ys.
Args:
log_dir (str): Log directory for csv file.
xcolumn (str): Which column should be the JSON x axis.
ycolumn (str): Which column should be the JSON y axis.
Returns:
list: List of x axis points.
list: List of y axis points.
| _read_csv | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _export_to_json(json_name, xs, xlabel, ys, ylabel, ys_std):
"""Save selected csv column to JSON preparing for automatic benchmarking.
Args:
json_name (str): The JSON file name.
xs (list): List of x axis points
xlabel (str): Label name for x axis.
ys (np.array): List of y axi... | Save selected csv column to JSON preparing for automatic benchmarking.
Args:
json_name (str): The JSON file name.
xs (list): List of x axis points
xlabel (str): Label name for x axis.
ys (np.array): List of y axis points
ylabel (str): Label name for y axis.
ys_std (n... | _export_to_json | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _upload_to_gcp_storage(exec_dir):
"""Upload all files to GCP storage under exec_dir folder.
Args:
exec_dir (str): The execution directory.
"""
_bucket = storage.Client().bucket('resl-garage-benchmarks')
exec_name = os.path.basename(exec_dir)
for folder_name in os.listdir(exec_dir)... | Upload all files to GCP storage under exec_dir folder.
Args:
exec_dir (str): The execution directory.
| _upload_to_gcp_storage | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def run(names):
"""Run selected benchmarks.
Args:
names (tuple): Benchmark names.
Raises:
BadParameter: if any run name is invalid or duplicated.
"""
if not names:
raise click.BadParameter('Empty names!')
if len(names) != len(set(names)):
raise click.BadParame... | Run selected benchmarks.
Args:
names (tuple): Benchmark names.
Raises:
BadParameter: if any run name is invalid or duplicated.
| run | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _get_all_options():
"""Return a dict containing all benchmark options.
Dict of (str: obj) representing benchmark name and its function object.
Returns:
dict: Benchmark options.
"""
d = {}
d.update(_get_runs_dict(benchmark_algos))
d.update(_get_runs_dict(benchmark_policies))
... | Return a dict containing all benchmark options.
Dict of (str: obj) representing benchmark name and its function object.
Returns:
dict: Benchmark options.
| _get_all_options | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _get_runs_dict(module):
"""Return a dict containing benchmark options of the module.
Dict of (str: obj) representing benchmark name and its function object.
Args:
module (object): Module object.
Returns:
dict: Benchmark options of the module.
"""
d = {}
for name, obj ... | Return a dict containing benchmark options of the module.
Dict of (str: obj) representing benchmark name and its function object.
Args:
module (object): Module object.
Returns:
dict: Benchmark options of the module.
| _get_runs_dict | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _echo_run_names(header, d):
"""Echo run names to the command line.
Args:
header (str): The header name.
d (dict): The dict containing benchmark options.
"""
click.echo('-----' + header + '-----')
for name in d:
click.echo(name)
click.echo() | Echo run names to the command line.
Args:
header (str): The header name.
d (dict): The dict containing benchmark options.
| _echo_run_names | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def ddpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow DDPG model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Rand... | Create garage TensorFlow DDPG model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| ddpg_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ddpg_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ddpg_garage_tf.py | MIT |
def her_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow HER model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create garage TensorFlow HER model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| her_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/her_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/her_garage_tf.py | MIT |
def ppo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| ppo_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_pytorch.py | MIT |
def ppo_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (in... | Create garage TensorFlow PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | ppo_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_tf.py | MIT |
def td3_garage_pytorch(ctxt, env_id, seed):
"""Create garage TensorFlow TD3 model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Localtrainer to create the
snapshotter.
env_id (str): Environment id of the task.
... | Create garage TensorFlow TD3 model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Localtrainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial... | td3_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_pytorch.py | MIT |
def td3_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow TD3 model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create garage TensorFlow TD3 model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| td3_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_tf.py | MIT |
def trpo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch TRPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
... | Create garage PyTorch TRPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the tria... | trpo_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_pytorch.py | MIT |
def trpo_garage_tf(ctxt, env_id, seed):
"""Create garage Tensorflow TROI model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage Tensorflow TROI model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | trpo_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_tf.py | MIT |
def vpg_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| vpg_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_pytorch.py | MIT |
def vpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (in... | Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | vpg_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_tf.py | MIT |
def continuous_mlp_baseline(ctxt, env_id, seed):
"""Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Ran... | Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/continuous_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/continuous_mlp_baseline.py | MIT |
def gaussian_cnn_baseline(ctxt, env_id, seed):
"""Create Gaussian CNN Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int... | Create Gaussian CNN Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_cnn_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_cnn_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_cnn_baseline.py | MIT |
def gaussian_mlp_baseline(ctxt, env_id, seed):
"""Create Gaussian MLP Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int... | Create Gaussian MLP Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_mlp_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_mlp_baseline.py | MIT |
def categorical_cnn_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (i... | Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_cnn_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_cnn_policy.py | MIT |
def categorical_lstm_policy(ctxt, env_id, seed):
"""Create Categorical LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed ... | Create Categorical LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_lstm_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_lstm_policy.py | MIT |
def categorical_mlp_policy(ctxt, env_id, seed):
"""Create Categorical MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (i... | Create Categorical MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_mlp_policy.py | MIT |
def continuous_mlp_policy(ctxt, env_id, seed):
"""Create Continuous MLP Policy on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create Continuous MLP Policy on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/continuous_mlp_policy.py | MIT |
def gaussian_gru_policy(ctxt, env_id, seed):
"""Create Gaussian GRU Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): R... | Create Gaussian GRU Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_gru_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_gru_policy.py | MIT |
def gaussian_lstm_policy(ctxt, env_id, seed):
"""Create Gaussian LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int):... | Create Gaussian LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_lstm_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_lstm_policy.py | MIT |
def gaussian_mlp_policy(ctxt, env_id, seed):
"""Create Gaussian MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): R... | Create Gaussian MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_mlp_policy.py | MIT |
def continuous_mlp_q_function(ctxt, env_id, seed):
"""Create Continuous MLP QFunction on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int):... | Create Continuous MLP QFunction on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_q_function | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/q_functions/continuous_mlp_q_function.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/q_functions/continuous_mlp_q_function.py | MIT |
def setup(self, algo, env):
"""Set up trainer for algorithm and environment.
This method saves algo and env within trainer and creates a sampler.
Note:
After setup() is called all variables in session should have been
initialized. setup() respects existing values in ses... | Set up trainer for algorithm and environment.
This method saves algo and env within trainer and creates a sampler.
Note:
After setup() is called all variables in session should have been
initialized. setup() respects existing values in session so
policy weights can ... | setup | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def obtain_episodes(self,
itr,
batch_size=None,
agent_update=None,
env_update=None):
"""Obtain one batch of episodes.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Nu... | Obtain one batch of episodes.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number of steps in batch. This is a hint that the
sampler may or may not respect.
agent_update (object): Value which will be passed into the
`agent_up... | obtain_episodes | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def obtain_samples(self,
itr,
batch_size=None,
agent_update=None,
env_update=None):
"""Obtain one batch of samples.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number o... | Obtain one batch of samples.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number of steps in batch.
This is a hint that the sampler may or may not respect.
agent_update (object): Value which will be passed into the
`agent_upd... | obtain_samples | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def save(self, epoch):
"""Save snapshot of current batch.
Args:
epoch (int): Epoch.
Raises:
NotSetupError: if save() is called before the trainer is set up.
"""
if not self._has_setup:
raise NotSetupError('Use setup() to setup trainer before... | Save snapshot of current batch.
Args:
epoch (int): Epoch.
Raises:
NotSetupError: if save() is called before the trainer is set up.
| save | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def restore(self, from_dir, from_epoch='last'):
"""Restore experiment from snapshot.
Args:
from_dir (str): Directory of the pickle file
to resume experiment from.
from_epoch (str or int): The epoch to restore from.
Can be 'first', 'last' or a numb... | Restore experiment from snapshot.
Args:
from_dir (str): Directory of the pickle file
to resume experiment from.
from_epoch (str or int): The epoch to restore from.
Can be 'first', 'last' or a number.
Not applicable when snapshot_mode='last... | restore | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def log_diagnostics(self, pause_for_plot=False):
"""Log diagnostics.
Args:
pause_for_plot (bool): Pause for plot.
"""
logger.log('Time %.2f s' % (time.time() - self._start_time))
logger.log('EpochTime %.2f s' % (time.time() - self._itr_start_time))
tabular.r... | Log diagnostics.
Args:
pause_for_plot (bool): Pause for plot.
| log_diagnostics | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def train(self,
n_epochs,
batch_size=None,
plot=False,
store_episodes=False,
pause_for_plot=False):
"""Start training.
Args:
n_epochs (int): Number of epochs.
batch_size (int or None): Number of environment st... | Start training.
Args:
n_epochs (int): Number of epochs.
batch_size (int or None): Number of environment steps in one batch.
plot (bool): Visualize an episode from the policy after each epoch.
store_episodes (bool): Save episodes in snapshot.
pause_for... | train | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def step_epochs(self):
"""Step through each epoch.
This function returns a magic generator. When iterated through, this
generator automatically performs services such as snapshotting and log
management. It is used inside train() in each algorithm.
The generator initializes two ... | Step through each epoch.
This function returns a magic generator. When iterated through, this
generator automatically performs services such as snapshotting and log
management. It is used inside train() in each algorithm.
The generator initializes two variables: `self.step_itr` and
... | step_epochs | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def resume(self,
n_epochs=None,
batch_size=None,
plot=None,
store_episodes=None,
pause_for_plot=None):
"""Resume from restored experiment.
This method provides the same interface as train().
If not specified, an argumen... | Resume from restored experiment.
This method provides the same interface as train().
If not specified, an argument will default to the
saved arguments from the last call to train().
Args:
n_epochs (int): Number of epochs.
batch_size (int): Number of environment... | resume | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def get_env_copy(self):
"""Get a copy of the environment.
Returns:
Environment: An environment instance.
"""
if self._env:
return cloudpickle.loads(cloudpickle.dumps(self._env))
else:
return None | Get a copy of the environment.
Returns:
Environment: An environment instance.
| get_env_copy | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def __enter__(self):
"""Set self.sess as the default session.
Returns:
TFTrainer: This trainer.
"""
if tf.compat.v1.get_default_session() is not self.sess:
self.sess.__enter__()
self.sess_entered = True
return self | Set self.sess as the default session.
Returns:
TFTrainer: This trainer.
| __enter__ | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def __exit__(self, exc_type, exc_val, exc_tb):
"""Leave session.
Args:
exc_type (str): Type.
exc_val (object): Value.
exc_tb (object): Traceback.
"""
if tf.compat.v1.get_default_session(
) is self.sess and self.sess_entered:
self.... | Leave session.
Args:
exc_type (str): Type.
exc_val (object): Value.
exc_tb (object): Traceback.
| __exit__ | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def setup(self, algo, env):
"""Set up trainer and sessions for algorithm and environment.
This method saves algo and env within trainer and creates a sampler,
and initializes all uninitialized variables in session.
Note:
After setup() is called all variables in session shou... | Set up trainer and sessions for algorithm and environment.
This method saves algo and env within trainer and creates a sampler,
and initializes all uninitialized variables in session.
Note:
After setup() is called all variables in session should have been
initialized. s... | setup | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def initialize_tf_vars(self):
"""Initialize all uninitialized variables in session."""
with tf.name_scope('initialize_tf_vars'):
uninited_set = [
e.decode() for e in self.sess.run(
tf.compat.v1.report_uninitialized_variables())
]
se... | Initialize all uninitialized variables in session. | initialize_tf_vars | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def get_step_type(cls, step_cnt, max_episode_length, done):
"""Determines the step type based on step cnt and done signal.
Args:
step_cnt (int): current step cnt of the environment.
max_episode_length (int): maximum episode length.
done (bool): the done signal return... | Determines the step type based on step cnt and done signal.
Args:
step_cnt (int): current step cnt of the environment.
max_episode_length (int): maximum episode length.
done (bool): the done signal returned by Environment.
Returns:
StepType: the step typ... | get_step_type | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
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 |
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