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def _exclude_regex(self, which):
"""Return a compiled regex for the given exclusion list."""
if which not in self._exclude_re:
excl_list = getattr(self.config, which + "_list")
self._exclude_re[which] = join_regex(excl_list)
return self._exclude_re[which] |
def save(self):
"""Save the collected coverage data to the data file."""
data_suffix = self.data_suffix
if data_suffix is True:
# If data_suffix was a simple true value, then make a suffix with
# plenty of distinguishing information. We do this here in
# `sav... |
def combine(self):
"""Combine together a number of similarly-named coverage data files.
All coverage data files whose name starts with `data_file` (from the
coverage() constructor) will be read, and combined together into the
current measurements.
"""
aliases = None
... |
def _harvest_data(self):
"""Get the collected data and reset the collector.
Also warn about various problems collecting data.
"""
if not self._measured:
return
self.data.add_line_data(self.collector.get_line_data())
self.data.add_arc_data(self.collector.get... |
def analysis(self, morf):
"""Like `analysis2` but doesn't return excluded line numbers."""
f, s, _, m, mf = self.analysis2(morf)
return f, s, m, mf |
def analysis2(self, morf):
"""Analyze a module.
`morf` is a module or a filename. It will be analyzed to determine
its coverage statistics. The return value is a 5-tuple:
* The filename for the module.
* A list of line numbers of executable statements.
* A list of lin... |
def _analyze(self, it):
"""Analyze a single morf or code unit.
Returns an `Analysis` object.
"""
self._harvest_data()
if not isinstance(it, CodeUnit):
it = code_unit_factory(it, self.file_locator)[0]
return Analysis(self, it) |
def report(self, morfs=None, show_missing=True, ignore_errors=None,
file=None, # pylint: disable=W0622
omit=None, include=None
):
"""Write a summary report to `file`.
Each module in `morfs` is listed, with counts of statements, ex... |
def annotate(self, morfs=None, directory=None, ignore_errors=None,
omit=None, include=None):
"""Annotate a list of modules.
Each module in `morfs` is annotated. The source is written to a new
file, named with a ",cover" suffix, with each line prefixed with a
marker ... |
def html_report(self, morfs=None, directory=None, ignore_errors=None,
omit=None, include=None, extra_css=None, title=None):
"""Generate an HTML report.
The HTML is written to `directory`. The file "index.html" is the
overview starting point, with links to more detailed page... |
def xml_report(self, morfs=None, outfile=None, ignore_errors=None,
omit=None, include=None):
"""Generate an XML report of coverage results.
The report is compatible with Cobertura reports.
Each module in `morfs` is included in the report. `outfile` is the
path to w... |
def sysinfo(self):
"""Return a list of (key, value) pairs showing internal information."""
import coverage as covmod
import platform, re
try:
implementation = platform.python_implementation()
except AttributeError:
implementation = "unknown"
inf... |
def display(*objs, **kwargs):
"""Display a Python object in all frontends.
By default all representations will be computed and sent to the frontends.
Frontends can decide which representation is used and how.
Parameters
----------
objs : tuple of objects
The Python objects to display.
... |
def display_pretty(*objs, **kwargs):
"""Display the pretty (default) representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw text data to
display.
raw : bool
Are the data objects raw data or Python objects ... |
def display_html(*objs, **kwargs):
"""Display the HTML representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw HTML data to
display.
raw : bool
Are the data objects raw data or Python objects that need to b... |
def display_svg(*objs, **kwargs):
"""Display the SVG representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw svg data to
display.
raw : bool
Are the data objects raw data or Python objects that need to be
... |
def display_png(*objs, **kwargs):
"""Display the PNG representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw png data to
display.
raw : bool
Are the data objects raw data or Python objects that need to be
... |
def display_jpeg(*objs, **kwargs):
"""Display the JPEG representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw JPEG data to
display.
raw : bool
Are the data objects raw data or Python objects that need to b... |
def display_latex(*objs, **kwargs):
"""Display the LaTeX representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw latex data to
display.
raw : bool
Are the data objects raw data or Python objects that need t... |
def display_json(*objs, **kwargs):
"""Display the JSON representation of an object.
Note that not many frontends support displaying JSON.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw json data to
display.
raw : bool
Are... |
def display_javascript(*objs, **kwargs):
"""Display the Javascript representation of an object.
Parameters
----------
objs : tuple of objects
The Python objects to display, or if raw=True raw javascript data to
display.
raw : bool
Are the data objects raw data or Python obje... |
def clear_output(stdout=True, stderr=True, other=True):
"""Clear the output of the current cell receiving output.
Optionally, each of stdout/stderr or other non-stream data (e.g. anything
produced by display()) can be excluded from the clear event.
By default, everything is cleared.
P... |
def reload(self):
"""Reload the raw data from file or URL."""
if self.filename is not None:
with open(self.filename, self._read_flags) as f:
self.data = f.read()
elif self.url is not None:
try:
import urllib2
response = urll... |
def pip_version_check(session):
"""Check for an update for pip.
Limit the frequency of checks to once per week. State is stored either in
the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix
of the pip script path.
"""
import pip # imported here to prevent circular import... |
def _find_cmd(cmd):
"""Find the full path to a command using which."""
path = sp.Popen(['/usr/bin/env', 'which', cmd],
stdout=sp.PIPE, stderr=sp.PIPE).communicate()[0]
return py3compat.bytes_to_str(path) |
def getoutput_pexpect(self, cmd):
"""Run a command and return its stdout/stderr as a string.
Parameters
----------
cmd : str
A command to be executed in the system shell.
Returns
-------
output : str
A string containing the combination of std... |
def system(self, cmd):
"""Execute a command in a subshell.
Parameters
----------
cmd : str
A command to be executed in the system shell.
Returns
-------
int : child's exitstatus
"""
# Get likely encoding for the output.
enc = DE... |
def forward_read_events(fd, context=None):
"""Forward read events from an FD over a socket.
This method wraps a file in a socket pair, so it can
be polled for read events by select (specifically zmq.eventloop.ioloop)
"""
if context is None:
context = zmq.Context.instance()
push = contex... |
def run(self):
"""Loop through lines in self.fd, and send them over self.sock."""
line = self.fd.readline()
# allow for files opened in unicode mode
if isinstance(line, unicode):
send = self.sock.send_unicode
else:
send = self.sock.send
while line:... |
def find_launcher_class(clsname, kind):
"""Return a launcher for a given clsname and kind.
Parameters
==========
clsname : str
The full name of the launcher class, either with or without the
module path, or an abbreviation (MPI, SSH, SGE, PBS, LSF,
WindowsHPC).
kind : str
... |
def start(self):
"""Start the app for the stop subcommand."""
try:
pid = self.get_pid_from_file()
except PIDFileError:
self.log.critical(
'Could not read pid file, cluster is probably not running.'
)
# Here I exit with a unusual exi... |
def build_launcher(self, clsname, kind=None):
"""import and instantiate a Launcher based on importstring"""
try:
klass = find_launcher_class(clsname, kind)
except (ImportError, KeyError):
self.log.fatal("Could not import launcher class: %r"%clsname)
self.exit(... |
def start(self):
"""Start the app for the engines subcommand."""
self.log.info("IPython cluster: started")
# First see if the cluster is already running
# Now log and daemonize
self.log.info(
'Starting engines with [daemon=%r]' % self.daemonize
)
# TO... |
def start(self):
"""Start the app for the start subcommand."""
# First see if the cluster is already running
try:
pid = self.get_pid_from_file()
except PIDFileError:
pass
else:
if self.check_pid(pid):
self.log.critical(
... |
def get_app_wx(*args, **kwargs):
"""Create a new wx app or return an exiting one."""
import wx
app = wx.GetApp()
if app is None:
if not kwargs.has_key('redirect'):
kwargs['redirect'] = False
app = wx.PySimpleApp(*args, **kwargs)
return app |
def is_event_loop_running_wx(app=None):
"""Is the wx event loop running."""
if app is None:
app = get_app_wx()
if hasattr(app, '_in_event_loop'):
return app._in_event_loop
else:
return app.IsMainLoopRunning() |
def start_event_loop_wx(app=None):
"""Start the wx event loop in a consistent manner."""
if app is None:
app = get_app_wx()
if not is_event_loop_running_wx(app):
app._in_event_loop = True
app.MainLoop()
app._in_event_loop = False
else:
app._in_event_loop = True |
def get_app_qt4(*args, **kwargs):
"""Create a new qt4 app or return an existing one."""
from IPython.external.qt_for_kernel import QtGui
app = QtGui.QApplication.instance()
if app is None:
if not args:
args = ([''],)
app = QtGui.QApplication(*args, **kwargs)
return app |
def is_event_loop_running_qt4(app=None):
"""Is the qt4 event loop running."""
if app is None:
app = get_app_qt4([''])
if hasattr(app, '_in_event_loop'):
return app._in_event_loop
else:
# Does qt4 provide a other way to detect this?
return False |
def start_event_loop_qt4(app=None):
"""Start the qt4 event loop in a consistent manner."""
if app is None:
app = get_app_qt4([''])
if not is_event_loop_running_qt4(app):
app._in_event_loop = True
app.exec_()
app._in_event_loop = False
else:
app._in_event_loop = Tr... |
def check_package(self, package, package_dir):
"""Check namespace packages' __init__ for declare_namespace"""
try:
return self.packages_checked[package]
except KeyError:
pass
init_py = _build_py.check_package(self, package, package_dir)
self.packages_chec... |
def blank_canvas(width, height):
"""Return a blank canvas to annotate.
:param width: xdim (int)
:param height: ydim (int)
:returns: :class:`jicbioimage.illustrate.Canvas`
"""
canvas = np.zeros((height, width, 3), dtype=np.uint8)
return canvas.view(Canvas) |
def draw_cross(self, position, color=(255, 0, 0), radius=4):
"""Draw a cross on the canvas.
:param position: (row, col) tuple
:param color: RGB tuple
:param radius: radius of the cross (int)
"""
y, x = position
for xmod in np.arange(-radius, radius+1, 1):
... |
def draw_line(self, pos1, pos2, color=(255, 0, 0)):
"""Draw a line between pos1 and pos2 on the canvas.
:param pos1: position 1 (row, col) tuple
:param pos2: position 2 (row, col) tuple
:param color: RGB tuple
"""
r1, c1 = tuple([int(round(i, 0)) for i in pos1])
... |
def text_at(self, text, position, color=(255, 255, 255),
size=12, antialias=False, center=False):
"""Write text at x, y top left corner position.
By default the x and y coordinates represent the top left hand corner
of the text. The text can be centered vertically and horizontal... |
def from_grayscale(im, channels_on=(True, True, True)):
"""Return a canvas from a grayscale image.
:param im: single channel image
:channels_on: channels to populate with input image
:returns: :class:`jicbioimage.illustrate.Canvas`
"""
xdim, ydim = im.shape
canva... |
def get_uuid(length=32, version=1):
"""
Returns a unique ID of a given length.
User `version=2` for cross-systems uniqueness.
"""
if version == 1:
return uuid.uuid1().hex[:length]
else:
return uuid.uuid4().hex[:length] |
def get_dict_to_encoded_url(data):
"""
Converts a dict to an encoded URL.
Example: given data = {'a': 1, 'b': 2}, it returns 'a=1&b=2'
"""
unicode_data = dict([(k, smart_str(v)) for k, v in data.items()])
encoded = urllib.urlencode(unicode_data)
return encoded |
def get_encoded_url_to_dict(string):
"""
Converts an encoded URL to a dict.
Example: given string = 'a=1&b=2' it returns {'a': 1, 'b': 2}
"""
data = urllib.parse.parse_qsl(string, keep_blank_values=True)
data = dict(data)
return data |
def get_unique_key_from_get(get_dict):
"""
Build a unique key from get data
"""
site = Site.objects.get_current()
key = get_dict_to_encoded_url(get_dict)
cache_key = '{}_{}'.format(site.domain, key)
return hashlib.md5(cache_key).hexdigest() |
def tobin(deci_num, len=32):
"""
Given a decimal number, returns a string bitfield of length = len
Example: given deci_num = 1 and len = 10, it return 0000000001
"""
bitstr = "".join(map(lambda y: str((deci_num >> y) & 1), range(len - 1, -1, -1)))
return bitstr |
def is_valid_email(email):
"""
Validates and email address.
Note: valid emails must follow the <name>@<domain><.extension> patterns.
"""
try:
validate_email(email)
except ValidationError:
return False
if simple_email_re.match(email):
return True
return False |
def get_domain(url):
""" Returns domain name portion of a URL """
if 'http' not in url.lower():
url = 'http://{}'.format(url)
return urllib.parse.urlparse(url).hostname |
def get_url_args(url):
""" Returns a dictionary from a URL params """
url_data = urllib.parse.urlparse(url)
arg_dict = urllib.parse.parse_qs(url_data.query)
return arg_dict |
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
... |
def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "... |
def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(conv(h... |
def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):
"""
Stack of fully-connected layers to be used in a policy / q-function approximator
Parameters:
----------
num_layers: int number of fully-connected layers (default: 2)
num_hidden: int ... |
def lstm(nlstm=128, layer_norm=False):
"""
Builds LSTM (Long-Short Term Memory) network to be used in a policy.
Note that the resulting function returns not only the output of the LSTM
(i.e. hidden state of lstm for each step in the sequence), but also a dictionary
with auxiliary tensors to be set a... |
def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
'''
convolutions-only net
Parameters:
----------
conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
Returns:
function that takes tensorflow tensor as input and re... |
def get_network_builder(name):
"""
If you want to register your own network outside models.py, you just need:
Usage Example:
-------------
from baselines.common.models import register
@register("your_network_name")
def your_network_define(**net_kwargs):
...
return network_fn... |
def mlp(hiddens=[], layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
hiddens: [int]
list of sizes of hidden layers
layer_norm: bool
if true applies layer normalization for every layer
as described in http... |
def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
convs: [(int, int, int)]
list of convolutional layers in form of
(num_outputs, kernel_size, stride)
hiddens: [int]
... |
def make_vec_env(env_id, env_type, num_env, seed,
wrapper_kwargs=None,
start_index=0,
reward_scale=1.0,
flatten_dict_observations=True,
gamestate=None):
"""
Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
""... |
def make_mujoco_env(env_id, seed, reward_scale=1.0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
rank = MPI.COMM_WORLD.Get_rank()
myseed = seed + 1000 * rank if seed is not None else None
set_global_seeds(myseed)
env = gym.make(env_id)
logger_path = None if logger.get_dir() ... |
def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), st... |
def common_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be au... |
def robotics_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--n... |
def parse_unknown_args(args):
"""
Parse arguments not consumed by arg parser into a dicitonary
"""
retval = {}
preceded_by_key = False
for arg in args:
if arg.startswith('--'):
if '=' in arg:
key = arg.split('=')[0][2:]
value = arg.split('=')[1... |
def clear_mpi_env_vars():
"""
from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment... |
def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, model_fn=None, **network_k... |
def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
"""
Demmel p 312
"""
p = b.copy()
r = b.copy()
x = np.zeros_like(b)
rdotr = r.dot(r)
fmtstr = "%10i %10.3g %10.3g"
titlestr = "%10s %10s %10s"
if verbose: print(titlestr % ("iter", "residual norm",... |
def observation_placeholder(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
batch_size: int size of the batch to be fed into input.... |
def observation_input(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space, and add input
encoder of the appropriate type.
'''
placeholder = observation_placeholder(ob_space, batch_size, name)
return placeholder... |
def encode_observation(ob_space, placeholder):
'''
Encode input in the way that is appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
placeholder: tf.placeholder observation input placeholder
'''
if isinstance(ob_space, Di... |
def generate_rollouts(self):
"""Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
policy acting on it accordingly.
"""
self.reset_all_rollouts()
# compute observations
o = np.empty((self.rollout_batch_size, self.dims['o']), np.float... |
def save_policy(self, path):
"""Pickles the current policy for later inspection.
"""
with open(path, 'wb') as f:
pickle.dump(self.policy, f) |
def logs(self, prefix='worker'):
"""Generates a dictionary that contains all collected statistics.
"""
logs = []
logs += [('success_rate', np.mean(self.success_history))]
if self.compute_Q:
logs += [('mean_Q', np.mean(self.Q_history))]
logs += [('episode', sel... |
def smooth(y, radius, mode='two_sided', valid_only=False):
'''
Smooth signal y, where radius is determines the size of the window
mode='twosided':
average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
mode='causal':
average over the window [max(index - radius, ... |
def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform one-sided (causal) EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]... |
def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform symmetric EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arg... |
def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
'''
load summaries of runs from a list of directories (including subdirectories)
Arguments:
enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: T... |
def plot_results(
allresults, *,
xy_fn=default_xy_fn,
split_fn=default_split_fn,
group_fn=default_split_fn,
average_group=False,
shaded_std=True,
shaded_err=True,
figsize=None,
legend_outside=False,
resample=0,
smooth_step=1.0
):
'''
Plot multiple Results objects
... |
def check_synced(localval, comm=None):
"""
It's common to forget to initialize your variables to the same values, or
(less commonly) if you update them in some other way than adam, to get them out of sync.
This function checks that variables on all MPI workers are the same, and raises
an AssertionEr... |
def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()} |
def obs_space_info(obs_space):
"""
Get dict-structured information about a gym.Space.
Returns:
A tuple (keys, shapes, dtypes):
keys: a list of dict keys.
shapes: a dict mapping keys to shapes.
dtypes: a dict mapping keys to dtypes.
"""
if isinstance(obs_space, gym.spac... |
def q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma):
"""
Calculates q_retrace targets
:param R: Rewards
:param D: Dones
:param q_i: Q values for actions taken
:param v: V values
:param rho_i: Importance weight for each action
:return: Q_retrace values
"""
rho_bar = batch_to... |
def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_regio... |
def apply_stats(self, statsUpdates):
""" compute stats and update/apply the new stats to the running average
"""
def updateAccumStats():
if self._full_stats_init:
return tf.cond(tf.greater(self.sgd_step, self._cold_iter), lambda: tf.group(*self._apply_stats(statsUpda... |
def tile_images(img_nhwc):
"""
Tile N images into one big PxQ image
(P,Q) are chosen to be as close as possible, and if N
is square, then P=Q.
input: img_nhwc, list or array of images, ndim=4 once turned into array
n = batch index, h = height, w = width, c = channel
returns:
big... |
def sum(self, start=0, end=None):
"""Returns arr[start] + ... + arr[end]"""
return super(SumSegmentTree, self).reduce(start, end) |
def find_prefixsum_idx(self, prefixsum):
"""Find the highest index `i` in the array such that
sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
if array values are probabilities, this function
allows to sample indexes according to the discrete
probability efficiently.
... |
def min(self, start=0, end=None):
"""Returns min(arr[start], ..., arr[end])"""
return super(MinSegmentTree, self).reduce(start, end) |
def value(self, t):
"""See Schedule.value"""
for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]):
if l_t <= t and t < r_t:
alpha = float(t - l_t) / (r_t - l_t)
return self._interpolation(l, r, alpha)
# t does not belong to any of ... |
def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
"""
Control a single environment instance using IPC and
shared memory.
"""
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[... |
def learn(
network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
load_path=None,
**network_kwargs):
'''
Ma... |
def sf01(arr):
"""
swap and then flatten axes 0 and 1
"""
s = arr.shape
return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:]) |
def step(self, observation, **extra_feed):
"""
Compute next action(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match ... |
def value(self, ob, *args, **kwargs):
"""
Compute value estimate(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match th... |
def pretty_eta(seconds_left):
"""Print the number of seconds in human readable format.
Examples:
2 days
2 hours and 37 minutes
less than a minute
Paramters
---------
seconds_left: int
Number of seconds to be converted to the ETA
Returns
-------
eta: str
Stri... |
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