code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
"""Controls which config parts are saved by Lightning loggers.
Additionally saves:
- Number of model parameters
:param object_dict: A dictionary containing the following objects:
- `"cfg"`: A DictConfig object containing the mai... | Controls which config parts are saved by Lightning loggers.
Additionally saves:
- Number of model parameters
:param object_dict: A dictionary containing the following objects:
- `"cfg"`: A DictConfig object containing the main config.
- `"model"`: The Lightning model.
- `"train... | log_hyperparameters | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/logging_utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/logging_utils.py | MIT |
def get_pylogger(name: str = __name__) -> logging.Logger:
"""Initializes a multi-GPU-friendly python command line logger.
:param name: The name of the logger, defaults to ``__name__``.
:return: A logger object.
"""
logger = logging.getLogger(name)
# this ensures all logging levels get marked ... | Initializes a multi-GPU-friendly python command line logger.
:param name: The name of the logger, defaults to ``__name__``.
:return: A logger object.
| get_pylogger | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/pylogger.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/pylogger.py | MIT |
def print_config_tree(
cfg: DictConfig,
print_order: Sequence[str] = (
"data",
"model",
"callbacks",
"logger",
"trainer",
"paths",
"extras",
),
resolve: bool = False,
save_to_file: bool = False,
) -> None:
"""Prints the contents of a DictCo... | Prints the contents of a DictConfig as a tree structure using the Rich library.
:param cfg: A DictConfig composed by Hydra.
:param print_order: Determines in what order config components are printed. Default is ``("data", "model",
"callbacks", "logger", "trainer", "paths", "extras")``.
:param resolve: ... | print_config_tree | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/rich_utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/rich_utils.py | MIT |
def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
"""Prompts user to input tags from command line if no tags are provided in config.
:param cfg: A DictConfig composed by Hydra.
:param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``.
"""
if ... | Prompts user to input tags from command line if no tags are provided in config.
:param cfg: A DictConfig composed by Hydra.
:param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``.
| enforce_tags | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/rich_utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/rich_utils.py | MIT |
def extras(cfg: DictConfig) -> None:
"""Applies optional utilities before the task is started.
Utilities:
- Ignoring python warnings
- Setting tags from command line
- Rich config printing
:param cfg: A DictConfig object containing the config tree.
"""
# return if no `extra... | Applies optional utilities before the task is started.
Utilities:
- Ignoring python warnings
- Setting tags from command line
- Rich config printing
:param cfg: A DictConfig object containing the config tree.
| extras | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/utils.py | MIT |
def task_wrapper(task_func: Callable) -> Callable:
"""Optional decorator that controls the failure behavior when executing the task function.
This wrapper can be used to:
- make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
- save the exceptio... | Optional decorator that controls the failure behavior when executing the task function.
This wrapper can be used to:
- make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
- save the exception to a `.log` file
- mark the run as failed with a... | task_wrapper | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/utils.py | MIT |
def get_metric_value(metric_dict: Dict[str, Any], metric_name: str) -> float:
"""Safely retrieves value of the metric logged in LightningModule.
:param metric_dict: A dict containing metric values.
:param metric_name: The name of the metric to retrieve.
:return: The value of the metric.
"""
if ... | Safely retrieves value of the metric logged in LightningModule.
:param metric_dict: A dict containing metric values.
:param metric_name: The name of the metric to retrieve.
:return: The value of the metric.
| get_metric_value | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/utils.py | MIT |
def get_user_data_dir(appname="matcha_tts"):
"""
Args:
appname (str): Name of application
Returns:
Path: path to user data directory
"""
MATCHA_HOME = os.environ.get("MATCHA_HOME")
if MATCHA_HOME is not None:
ans = Path(MATCHA_HOME).expanduser().resolve(strict=False)
... |
Args:
appname (str): Name of application
Returns:
Path: path to user data directory
| get_user_data_dir | python | abus-aikorea/voice-pro | third_party/Matcha-TTS/matcha/utils/utils.py | https://github.com/abus-aikorea/voice-pro/blob/master/third_party/Matcha-TTS/matcha/utils/utils.py | MIT |
def get_args():
"""
Description:
Parses arguments at command line.
Parameters:
None
Return:
args - the arguments parsed
"""
parser = argparse.ArgumentParser()
parser.add_argument('--mode', dest='mode', type=str, default='train') # can be 'train' or 'test'
parser.add_argument('--actor_... |
Description:
Parses arguments at command line.
Parameters:
None
Return:
args - the arguments parsed
| get_args | python | ericyangyu/PPO-for-Beginners | arguments.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/arguments.py | MIT |
def _log_summary(ep_len, ep_ret, ep_num):
"""
Print to stdout what we've logged so far in the most recent episode.
Parameters:
None
Return:
None
"""
# Round decimal places for more aesthetic logging messages
ep_len = str(round(ep_len, 2))
ep_ret = str(round(ep_ret, 2))
# Print logging st... |
Print to stdout what we've logged so far in the most recent episode.
Parameters:
None
Return:
None
| _log_summary | python | ericyangyu/PPO-for-Beginners | eval_policy.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/eval_policy.py | MIT |
def rollout(policy, env, render):
"""
Returns a generator to roll out each episode given a trained policy and
environment to test on.
Parameters:
policy - The trained policy to test
env - The environment to evaluate the policy on
render - Specifies whether to render or not
Return:
A generator ... |
Returns a generator to roll out each episode given a trained policy and
environment to test on.
Parameters:
policy - The trained policy to test
env - The environment to evaluate the policy on
render - Specifies whether to render or not
Return:
A generator object rollout, or iterable, which wil... | rollout | python | ericyangyu/PPO-for-Beginners | eval_policy.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/eval_policy.py | MIT |
def eval_policy(policy, env, render=False):
"""
The main function to evaluate our policy with. It will iterate a generator object
"rollout", which will simulate each episode and return the most recent episode's
length and return. We can then log it right after. And yes, eval_policy will run
forever until you k... |
The main function to evaluate our policy with. It will iterate a generator object
"rollout", which will simulate each episode and return the most recent episode's
length and return. We can then log it right after. And yes, eval_policy will run
forever until you kill the process.
Parameters:
policy - The... | eval_policy | python | ericyangyu/PPO-for-Beginners | eval_policy.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/eval_policy.py | MIT |
def train(env, hyperparameters, actor_model, critic_model):
"""
Trains the model.
Parameters:
env - the environment to train on
hyperparameters - a dict of hyperparameters to use, defined in main
actor_model - the actor model to load in if we want to continue training
critic_model - the critic model t... |
Trains the model.
Parameters:
env - the environment to train on
hyperparameters - a dict of hyperparameters to use, defined in main
actor_model - the actor model to load in if we want to continue training
critic_model - the critic model to load in if we want to continue training
Return:
None
| train | python | ericyangyu/PPO-for-Beginners | main.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/main.py | MIT |
def test(env, actor_model):
"""
Tests the model.
Parameters:
env - the environment to test the policy on
actor_model - the actor model to load in
Return:
None
"""
print(f"Testing {actor_model}", flush=True)
# If the actor model is not specified, then exit
if actor_model == '':
print(f"Didn't sp... |
Tests the model.
Parameters:
env - the environment to test the policy on
actor_model - the actor model to load in
Return:
None
| test | python | ericyangyu/PPO-for-Beginners | main.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/main.py | MIT |
def main(args):
"""
The main function to run.
Parameters:
args - the arguments parsed from command line
Return:
None
"""
# NOTE: Here's where you can set hyperparameters for PPO. I don't include them as part of
# ArgumentParser because it's too annoying to type them every time at command line. Instead... |
The main function to run.
Parameters:
args - the arguments parsed from command line
Return:
None
| main | python | ericyangyu/PPO-for-Beginners | main.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/main.py | MIT |
def __init__(self, in_dim, out_dim):
"""
Initialize the network and set up the layers.
Parameters:
in_dim - input dimensions as an int
out_dim - output dimensions as an int
Return:
None
"""
super(FeedForwardNN, self).__init__()
self.layer1 = nn.Linear(in_dim, 64)
self.layer2 = nn.Linea... |
Initialize the network and set up the layers.
Parameters:
in_dim - input dimensions as an int
out_dim - output dimensions as an int
Return:
None
| __init__ | python | ericyangyu/PPO-for-Beginners | network.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/network.py | MIT |
def forward(self, obs):
"""
Runs a forward pass on the neural network.
Parameters:
obs - observation to pass as input
Return:
output - the output of our forward pass
"""
# Convert observation to tensor if it's a numpy array
if isinstance(obs, np.ndarray):
obs = torch.tensor(obs, dtype=torc... |
Runs a forward pass on the neural network.
Parameters:
obs - observation to pass as input
Return:
output - the output of our forward pass
| forward | python | ericyangyu/PPO-for-Beginners | network.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/network.py | MIT |
def __init__(self, policy_class, env, **hyperparameters):
"""
Initializes the PPO model, including hyperparameters.
Parameters:
policy_class - the policy class to use for our actor/critic networks.
env - the environment to train on.
hyperparameters - all extra arguments passed into PPO that should ... |
Initializes the PPO model, including hyperparameters.
Parameters:
policy_class - the policy class to use for our actor/critic networks.
env - the environment to train on.
hyperparameters - all extra arguments passed into PPO that should be hyperparameters.
Returns:
None
| __init__ | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def learn(self, total_timesteps):
"""
Train the actor and critic networks. Here is where the main PPO algorithm resides.
Parameters:
total_timesteps - the total number of timesteps to train for
Return:
None
"""
print(f"Learning... Running {self.max_timesteps_per_episode} timesteps per episode, ... |
Train the actor and critic networks. Here is where the main PPO algorithm resides.
Parameters:
total_timesteps - the total number of timesteps to train for
Return:
None
| learn | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def rollout(self):
"""
Too many transformers references, I'm sorry. This is where we collect the batch of data
from simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch
of data each time we iterate the actor/critic networks.
Parameters:
None
Return:
batch_obs ... |
Too many transformers references, I'm sorry. This is where we collect the batch of data
from simulation. Since this is an on-policy algorithm, we'll need to collect a fresh batch
of data each time we iterate the actor/critic networks.
Parameters:
None
Return:
batch_obs - the observations colle... | rollout | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def compute_rtgs(self, batch_rews):
"""
Compute the Reward-To-Go of each timestep in a batch given the rewards.
Parameters:
batch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode)
Return:
batch_rtgs - the rewards to go, Shape: (number of timesteps in batch)... |
Compute the Reward-To-Go of each timestep in a batch given the rewards.
Parameters:
batch_rews - the rewards in a batch, Shape: (number of episodes, number of timesteps per episode)
Return:
batch_rtgs - the rewards to go, Shape: (number of timesteps in batch)
| compute_rtgs | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def get_action(self, obs):
"""
Queries an action from the actor network, should be called from rollout.
Parameters:
obs - the observation at the current timestep
Return:
action - the action to take, as a numpy array
log_prob - the log probability of the selected action in the distribution
"""... |
Queries an action from the actor network, should be called from rollout.
Parameters:
obs - the observation at the current timestep
Return:
action - the action to take, as a numpy array
log_prob - the log probability of the selected action in the distribution
| get_action | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def evaluate(self, batch_obs, batch_acts):
"""
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recently collected... |
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recently collected batch as a tensor.
Shape: (number of tim... | evaluate | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def _init_hyperparameters(self, hyperparameters):
"""
Initialize default and custom values for hyperparameters
Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters defined below with custom values.
Return:
None
"""
... |
Initialize default and custom values for hyperparameters
Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters defined below with custom values.
Return:
None
| _init_hyperparameters | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def _log_summary(self):
"""
Print to stdout what we've logged so far in the most recent batch.
Parameters:
None
Return:
None
"""
# Calculate logging values. I use a few python shortcuts to calculate each value
# without explaining since it's not too important to PPO; feel free to look it over... |
Print to stdout what we've logged so far in the most recent batch.
Parameters:
None
Return:
None
| _log_summary | python | ericyangyu/PPO-for-Beginners | ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/ppo.py | MIT |
def get_file_locations():
"""
Gets the absolute paths of each data file to graph.
Parameters:
None
Return:
paths - a dict with the following structure:
{
env: {
seeds: absolute_seeds_file_path
stable_baseli... |
Gets the absolute paths of each data file to graph.
Parameters:
None
Return:
paths - a dict with the following structure:
{
env: {
seeds: absolute_seeds_file_path
stable_baselines: [absolute_file_paths_to_data... | get_file_locations | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def extract_ppo_for_beginners_data(env, filename):
"""
Extract the total timesteps and average episodic return from the logging
data specified to PPO for Beginners.
Parameters:
env - The environment we're currently graphing.
filename - The file containing data. Shoul... |
Extract the total timesteps and average episodic return from the logging
data specified to PPO for Beginners.
Parameters:
env - The environment we're currently graphing.
filename - The file containing data. Should be "seed_xxx.txt" such that the
... | extract_ppo_for_beginners_data | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def extract_stable_baselines_data(env, filename):
"""
Extract the total timesteps and average episodic return from the logging
data specified to Stable Baselines PPO2.
Parameters:
env - The environment we're currently graphing.
filename - The file containing data. Sh... |
Extract the total timesteps and average episodic return from the logging
data specified to Stable Baselines PPO2.
Parameters:
env - The environment we're currently graphing.
filename - The file containing data. Should be "seed_xxx.txt" such that the
... | extract_stable_baselines_data | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def calculate_lower_bounds(x_s, y_s):
"""
Calculate lower bounds of total timesteps and average episodic
return per iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed.
Re... |
Calculate lower bounds of total timesteps and average episodic
return per iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed.
Return:
Lower bounds of both x_s a... | calculate_lower_bounds | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def calculate_upper_bounds(x_s, y_s):
"""
Calculate upper bounds of total timesteps and average episodic
return per iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed.
Re... |
Calculate upper bounds of total timesteps and average episodic
return per iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed.
Return:
Upper bounds of both x_s a... | calculate_upper_bounds | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def calculate_means(x_s, y_s):
"""
Calculate mean of each total timestep and average episodic return over all
trials at each iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed
... |
Calculate mean of each total timestep and average episodic return over all
trials at each iteration.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed
Return:
Means of x_... | calculate_means | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def clip_data(x_s, y_s):
"""
In the case that there are different number of iterations
across learning trials, clip all trials to the length of the shortest
trial.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of ... |
In the case that there are different number of iterations
across learning trials, clip all trials to the length of the shortest
trial.
Parameters:
x_s - A list of lists of total timesteps so far per seed.
y_s - A list of lists of average episodic return per seed... | clip_data | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def extract_data(paths):
"""
Extracts data from all the files, and returns a generator object
extract_data to iterably return data for each environment.
Number of iterations should equal number of environments in graph_data.
Parameters:
paths - Contains the paths to eac... |
Extracts data from all the files, and returns a generator object
extract_data to iterably return data for each environment.
Number of iterations should equal number of environments in graph_data.
Parameters:
paths - Contains the paths to each data file. Check function desc... | extract_data | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def graph_data(paths):
"""
Graphs the data with matplotlib. Will display on screen for user to screenshot.
Parameters:
paths - Contains the paths to each data file. Check function description of
get_file_locations() to see how paths is structured.
Return:
... |
Graphs the data with matplotlib. Will display on screen for user to screenshot.
Parameters:
paths - Contains the paths to each data file. Check function description of
get_file_locations() to see how paths is structured.
Return:
None
| graph_data | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def main():
"""
Main function to get file locations and graph the data.
Parameters:
None
Return:
None
"""
# Extract absolute file paths
paths = get_file_locations()
# Graph the data from the file paths extracted
graph_data(paths) |
Main function to get file locations and graph the data.
Parameters:
None
Return:
None
| main | python | ericyangyu/PPO-for-Beginners | graph_code/make_graph.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/make_graph.py | MIT |
def train_stable_baselines(args):
"""
Trains with PPO2 on specified environment.
Parameters:
args - the arguments defined in main.
Return:
None
"""
# Import stable baselines
from stable_baselines import PPO2
from stable_baselines.common.callbacks import CheckpointCallback
from stable_baselines.commo... |
Trains with PPO2 on specified environment.
Parameters:
args - the arguments defined in main.
Return:
None
| train_stable_baselines | python | ericyangyu/PPO-for-Beginners | graph_code/run.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/run.py | MIT |
def train_ppo_for_beginners(args):
"""
Trains with PPO for Beginners on specified environment.
Parameters:
args - the arguments defined in main.
Return:
None
"""
# Import ppo for beginners
from ppo_for_beginners.ppo import PPO
from ppo_for_beginners.network import FeedForwardNN
# Store hyperparamet... |
Trains with PPO for Beginners on specified environment.
Parameters:
args - the arguments defined in main.
Return:
None
| train_ppo_for_beginners | python | ericyangyu/PPO-for-Beginners | graph_code/run.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/run.py | MIT |
def main(args):
"""
An intermediate function that will call either PPO2 learn or PPO for Beginners learn.
Parameters:
args - the arguments defined below
Return:
None
"""
if args.code == 'stable_baselines_ppo2':
train_stable_baselines(args)
elif args.code == 'ppo_for_beginners':
train_ppo_for_begin... |
An intermediate function that will call either PPO2 learn or PPO for Beginners learn.
Parameters:
args - the arguments defined below
Return:
None
| main | python | ericyangyu/PPO-for-Beginners | graph_code/run.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/run.py | MIT |
def evaluate(self, batch_obs, batch_acts, batch_rtgs):
"""
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recent... |
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recently collected batch as a tensor.
Shape: (number of tim... | evaluate | python | ericyangyu/PPO-for-Beginners | graph_code/ppo_for_beginners/ppo.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/graph_code/ppo_for_beginners/ppo.py | MIT |
def __init__(self, policy_class, env, **hyperparameters):
"""
Initializes the PPO model, including hyperparameters.
Parameters:
policy_class - the policy class to use for our actor/critic networks.
env - the environment to train on.
hyperp... |
Initializes the PPO model, including hyperparameters.
Parameters:
policy_class - the policy class to use for our actor/critic networks.
env - the environment to train on.
hyperparameters - all extra arguments passed into PPO that should be hyperp... | __init__ | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def learn(self, total_timesteps):
"""
Train the actor and critic networks. Here is where the main PPO algorithm resides.
Parameters:
total_timesteps - the total number of timesteps to train for
Return:
None
"""
print(f"Learnin... |
Train the actor and critic networks. Here is where the main PPO algorithm resides.
Parameters:
total_timesteps - the total number of timesteps to train for
Return:
None
| learn | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def get_action(self, obs):
"""
Queries an action from the actor network, should be called from rollout.
Parameters:
obs - the observation at the current timestep
Return:
action - the action to take, as a numpy array
log_prob -... |
Queries an action from the actor network, should be called from rollout.
Parameters:
obs - the observation at the current timestep
Return:
action - the action to take, as a numpy array
log_prob - the log probability of the selected a... | get_action | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def evaluate(self, batch_obs, batch_acts):
"""
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_o... |
Estimate the values of each observation, and the log probs of
each action in the most recent batch with the most recent
iteration of the actor network. Should be called from learn.
Parameters:
batch_obs - the observations from the most recently collected... | evaluate | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def _init_hyperparameters(self, hyperparameters):
"""
Initialize default and custom values for hyperparameters
Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters ... |
Initialize default and custom values for hyperparameters
Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters defined below with custom values.
Return:
... | _init_hyperparameters | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def _log_summary(self):
"""
Print to stdout what we've logged so far in the most recent batch.
Parameters:
None
Return:
None
"""
# Calculate logging values. I use a few python shortcuts to calculate each value
# withou... |
Print to stdout what we've logged so far in the most recent batch.
Parameters:
None
Return:
None
| _log_summary | python | ericyangyu/PPO-for-Beginners | part4/ppo_for_beginners/ppo_optimized.py | https://github.com/ericyangyu/PPO-for-Beginners/blob/master/part4/ppo_for_beginners/ppo_optimized.py | MIT |
def _validate_args(
object_height: float,
shadow_length: float,
date_time: datetime,
) -> None:
"""
Validate the text search CLI arguments, raises an error if the arguments are invalid.
"""
if not object_height:
raise ValueError("Object height cannot be empty")
if not shadow_leng... |
Validate the text search CLI arguments, raises an error if the arguments are invalid.
| _validate_args | python | bellingcat/ShadowFinder | src/shadowfinder/cli.py | https://github.com/bellingcat/ShadowFinder/blob/master/src/shadowfinder/cli.py | MIT |
def find(
object_height: float,
shadow_length: float,
date: str,
time: str,
time_format: str = "utc",
grid: str = "timezone_grid.json",
) -> None:
"""
Find the shadow length of an object given its height and the date and time.
:param object_hei... |
Find the shadow length of an object given its height and the date and time.
:param object_height: Height of the object in arbitrary units
:param shadow_length: Length of the shadow in arbitrary units
:param date: Date in the format YYYY-MM-DD
:param time: UTC Time in the format ... | find | python | bellingcat/ShadowFinder | src/shadowfinder/cli.py | https://github.com/bellingcat/ShadowFinder/blob/master/src/shadowfinder/cli.py | MIT |
def find_sun(
sun_altitude_angle: float,
date: str,
time: str,
time_format: str = "utc",
grid: str = "timezene_grid.json",
) -> None:
"""
Locate a shadow based on the solar altitude angle and the date and time.
:param sun_altitude_angle: Sun altitude a... |
Locate a shadow based on the solar altitude angle and the date and time.
:param sun_altitude_angle: Sun altitude angle in degrees
:param date: Date in the format YYYY-MM-DD
:param time: UTC Time in the format HH:MM:SS
| find_sun | python | bellingcat/ShadowFinder | src/shadowfinder/cli.py | https://github.com/bellingcat/ShadowFinder/blob/master/src/shadowfinder/cli.py | MIT |
def generate_timezone_grid(
grid: str = "timezone_grid.json",
) -> None:
"""
Generate a timezone grid file.
:param grid: File path to save the timezone grid.
"""
shadow_finder = ShadowFinder()
shadow_finder.generate_timezone_grid()
shadow_finder.save_... |
Generate a timezone grid file.
:param grid: File path to save the timezone grid.
| generate_timezone_grid | python | bellingcat/ShadowFinder | src/shadowfinder/cli.py | https://github.com/bellingcat/ShadowFinder/blob/master/src/shadowfinder/cli.py | MIT |
def test_executable_without_args():
"""Tests that running shadowfinder without any arguments returns the CLI's help string and 0 exit code."""
# GIVEN
expected = """
NAME
shadowfinder
SYNOPSIS
shadowfinder COMMAND
COMMANDS
COMMAND is one of the following:
find
Find the shadow leng... | Tests that running shadowfinder without any arguments returns the CLI's help string and 0 exit code. | test_executable_without_args | python | bellingcat/ShadowFinder | tests/test_executable.py | https://github.com/bellingcat/ShadowFinder/blob/master/tests/test_executable.py | MIT |
def test_creation_with_valid_arguments_should_pass():
"""Baseline test to assert that we can create an instance of ShadowFinder with only object height, shadow length,
and a datetime object."""
# GIVEN
object_height = 6
shadow_length = 3.2
date_time = datetime.now()
# WHEN / THEN
Shadow... | Baseline test to assert that we can create an instance of ShadowFinder with only object height, shadow length,
and a datetime object. | test_creation_with_valid_arguments_should_pass | python | bellingcat/ShadowFinder | tests/test_shadowfinder.py | https://github.com/bellingcat/ShadowFinder/blob/master/tests/test_shadowfinder.py | MIT |
def test_huber_loss(self):
"""Test of huber loss
huber_loss() allows two types of inputs:
- `y_target` and `y_pred`
- `diff`
"""
# [1, 1] -> [0.5, 0.5]
loss = huber_loss(np.array([1., 1.]), delta=1.)
np.testing.assert_array_equal(
np.array([0.5... | Test of huber loss
huber_loss() allows two types of inputs:
- `y_target` and `y_pred`
- `diff`
| test_huber_loss | python | keiohta/tf2rl | tests/misc/test_huber_loss.py | https://github.com/keiohta/tf2rl/blob/master/tests/misc/test_huber_loss.py | MIT |
def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
input_data=None, expected_output=None,
expected_output_dtype=None, custom_objects=None):
"""Test routine for a layer with a single input and single output.
Arguments:
layer_cls: Layer class object.
... | Test routine for a layer with a single input and single output.
Arguments:
layer_cls: Layer class object.
kwargs: Optional dictionary of keyword arguments for instantiating the
layer.
input_shape: Input shape tuple.
input_dtype: Data type of the input data.
input_data: Numpy a... | layer_test | python | keiohta/tf2rl | tests/networks/utils.py | https://github.com/keiohta/tf2rl/blob/master/tests/networks/utils.py | MIT |
def explorer(global_rb, queue, trained_steps, is_training_done,
lock, env_fn, policy_fn, set_weights_fn, noise_level,
n_env=64, n_thread=4, buffer_size=1024, episode_max_steps=1000, gpu=0):
"""Collect transitions and store them to prioritized replay buffer.
Args:
global_rb: mu... | Collect transitions and store them to prioritized replay buffer.
Args:
global_rb: multiprocessing.managers.AutoProxy[PrioritizedReplayBuffer]
Prioritized replay buffer sharing with multiple explorers and only one learner.
This object is shared over processes, so it must be locked wh... | explorer | python | keiohta/tf2rl | tf2rl/algos/apex.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/apex.py | MIT |
def learner(global_rb, trained_steps, is_training_done,
lock, env, policy_fn, get_weights_fn,
n_training, update_freq, evaluation_freq, gpu, queues):
"""Update network weights using samples collected by explorers.
Args:
global_rb: multiprocessing.managers.AutoProxy[PrioritizedRe... | Update network weights using samples collected by explorers.
Args:
global_rb: multiprocessing.managers.AutoProxy[PrioritizedReplayBuffer]
Prioritized replay buffer sharing with multiple explorers and only one learner.
This object is shared over processes, so it must be locked when t... | learner | python | keiohta/tf2rl | tf2rl/algos/apex.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/apex.py | MIT |
def evaluator(is_training_done, env, policy_fn, set_weights_fn, queue, gpu,
save_model_interval=int(1e6), n_evaluation=10, episode_max_steps=1000,
show_test_progress=False):
"""Evaluate trained network weights periodically.
Args:
is_training_done: multiprocessing.Event
... | Evaluate trained network weights periodically.
Args:
is_training_done: multiprocessing.Event
multiprocessing.Event object to share the status of training.
env: Open-AI gym compatible environment
Environment object.
policy_fn: function
Method object to gen... | evaluator | python | keiohta/tf2rl | tf2rl/algos/apex.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/apex.py | MIT |
def __init__(self, eta=0.05, name="BiResDDPG", **kwargs):
"""
Initialize BiResDDPG agent
Args:
eta (float): Gradients mixing factor.
name (str): Name of agent. The default is ``"BiResDDPG"``.
state_shape (iterable of int):
action_dim (int):
... |
Initialize BiResDDPG agent
Args:
eta (float): Gradients mixing factor.
name (str): Name of agent. The default is ``"BiResDDPG"``.
state_shape (iterable of int):
action_dim (int):
max_action (float): Size of maximum action. (``-max_action`` <=... | __init__ | python | keiohta/tf2rl | tf2rl/algos/bi_res_ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/bi_res_ddpg.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns:
np.ndarray: Sum of two TD errors.
"""
td_error1,... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns:
np.ndarray: Sum of two TD errors.
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/bi_res_ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/bi_res_ddpg.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = DDPG.get_argument(parser)
... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/algos/bi_res_ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/bi_res_ddpg.py | MIT |
def __init__(
self,
state_shape,
action_dim,
q_func=None,
name="DQN",
lr=0.001,
adam_eps=1e-07,
units=(32, 32),
epsilon=0.1,
epsilon_min=None,
epsilon_decay_step=int(1e6),
n_wa... |
Initialize Categorical DQN
Args:
state_shape (iterable of int): Observation space shape
action_dim (int): Dimension of discrete action
q_function (QFunc): Custom Q function class. If ``None`` (default), Q function is constructed with ``QFunc``.
name (str... | __init__ | python | keiohta/tf2rl | tf2rl/algos/categorical_dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/categorical_dqn.py | MIT |
def get_action(self, state, test=False, tensor=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
tensor (bool): When ``True``, return type is ``tf.Tensor``
Returns:
... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
tensor (bool): When ``True``, return type is ``tf.Tensor``
Returns:
tf.Tensor or np.ndarray or float: Selected action
| get_action | python | keiohta/tf2rl | tf2rl/algos/categorical_dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/categorical_dqn.py | MIT |
def train(self, states, actions, next_states, rewards, done, weights=None):
"""
Train DQN
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is N... |
Train DQN
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/categorical_dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/categorical_dqn.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
"""
# TODO: fix this ugly con... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/categorical_dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/categorical_dqn.py | MIT |
def _compute_td_error_body(self, states, actions, next_states, rewards, dones):
"""
Args:
states:
actions:
next_states:
rewards:
Shape should be (batch_size, 1)
dones:
Shape should be (batch_size, 1)
Ret... |
Args:
states:
actions:
next_states:
rewards:
Shape should be (batch_size, 1)
dones:
Shape should be (batch_size, 1)
Returns:
| _compute_td_error_body | python | keiohta/tf2rl | tf2rl/algos/categorical_dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/categorical_dqn.py | MIT |
def __init__(self,
*args,
**kwargs):
"""
Initialize CURL
Args:
action_dim (int):
obs_shape: (iterable of int): The default is ``(84, 84, 9)``
n_conv_layers (int): Number of convolutional layers at encoder. The default is ``4`... |
Initialize CURL
Args:
action_dim (int):
obs_shape: (iterable of int): The default is ``(84, 84, 9)``
n_conv_layers (int): Number of convolutional layers at encoder. The default is ``4``
n_conv_filters (int): Number of filters in convolutional layers. The... | __init__ | python | keiohta/tf2rl | tf2rl/algos/curl_sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/curl_sac.py | MIT |
def train(self, states, actions, next_states, rewards, dones, weights=None):
"""
Train CURL
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is... |
Train CURL
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/curl_sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/curl_sac.py | MIT |
def __init__(
self,
state_shape,
action_dim,
name="DDPG",
max_action=1.,
lr_actor=0.001,
lr_critic=0.001,
actor_units=(400, 300),
critic_units=(400, 300),
sigma=0.1,
tau=0.005,
... |
Initialize DDPG agent
Args:
state_shape (iterable of int):
action_dim (int):
name (str): Name of agent. The default is ``"DDPG"``.
max_action (float): Size of maximum action. (``-max_action`` <= action <= ``max_action``). The degault is ``1``.
... | __init__ | python | keiohta/tf2rl | tf2rl/algos/ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ddpg.py | MIT |
def train(self, states, actions, next_states, rewards, done, weights=None):
"""
Train DDPG
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is ... |
Train DDPG
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ddpg.py | MIT |
def __init__(
self,
state_shape,
action_dim,
q_func=None,
name="DQN",
lr=0.001,
adam_eps=1e-07,
units=(32, 32),
epsilon=0.1,
epsilon_min=None,
epsilon_decay_step=int(1e6),
n_wa... |
Initialize DQN agent
Args:
state_shape (iterable of int): Observation space shape
action_dim (int): Dimension of discrete action
q_function (QFunc): Custom Q function class. If ``None`` (default), Q function is constructed with ``QFunc``.
name (str): Nam... | __init__ | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def __init__(
self,
state_shape,
units=(32, 32),
lr=0.001,
enable_sn=False,
name="GAIfO",
**kwargs):
"""
Initialize GAIfO
Args:
state_shape (iterable of int):
action_dim (int):
... |
Initialize GAIfO
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``0.001``
enable_sn (bool): Whether enable Spectral Normalization. The defa... | __init__ | python | keiohta/tf2rl | tf2rl/algos/gaifo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gaifo.py | MIT |
def inference(self, states, actions, next_states):
"""
Infer Reward with GAIfO
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
"""
assert states.shape == next_states.shape
if states.ndim == 1:
... |
Infer Reward with GAIfO
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
| inference | python | keiohta/tf2rl | tf2rl/algos/gaifo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gaifo.py | MIT |
def __init__(
self,
state_shape,
action_dim,
units=[32, 32],
lr=0.001,
enable_sn=False,
name="GAIL",
**kwargs):
"""
Initialize GAIL
Args:
state_shape (iterable of int):
action... |
Initialize GAIL
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``[32, 32]``
lr (float): Learning rate. The default is ``0.001``
enable_sn (bool): Whether enable Spectral Normalization. The defai... | __init__ | python | keiohta/tf2rl | tf2rl/algos/gail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gail.py | MIT |
def inference(self, states, actions, next_states):
"""
Infer Reward with GAIL
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
"""
if states.ndim == actions.ndim == 1:
states = np.expand_dims(states,... |
Infer Reward with GAIL
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
| inference | python | keiohta/tf2rl | tf2rl/algos/gail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gail.py | MIT |
def __init__(
self,
clip=True,
clip_ratio=0.2,
name="PPO",
**kwargs):
"""
Initialize PPO
Args:
clip (bool): Whether clip or not. The default is ``True``.
clip_ratio (float): Probability ratio is clipped between ... |
Initialize PPO
Args:
clip (bool): Whether clip or not. The default is ``True``.
clip_ratio (float): Probability ratio is clipped between ``1-clip_ratio`` and ``1+clip_ratio``.
name (str): Name of agent. The default is ``"PPO"``.
state_shape (iterable of ... | __init__ | python | keiohta/tf2rl | tf2rl/algos/ppo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ppo.py | MIT |
def train(self, states, actions, advantages, logp_olds, returns):
"""
Train PPO
Args:
states
actions
advantages
logp_olds
returns
"""
# Train actor and critic
if self.actor_critic is not None:
actor_... |
Train PPO
Args:
states
actions
advantages
logp_olds
returns
| train | python | keiohta/tf2rl | tf2rl/algos/ppo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ppo.py | MIT |
def __init__(
self,
state_shape,
action_dim,
name="SAC",
max_action=1.,
lr=3e-4,
lr_alpha=3e-4,
actor_units=(256, 256),
critic_units=(256, 256),
tau=5e-3,
alpha=.2,
auto_alpha=... |
Initialize SAC
Args:
state_shape (iterable of int):
action_dim (int):
name (str): Name of network. The default is ``"SAC"``
max_action (float):
lr (float): Learning rate. The default is ``3e-4``.
lr_alpha (alpha): Learning rate fo... | __init__ | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def get_action(self, state, test=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
"""
assert isinstance(state,... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
| get_action | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def train(self, states, actions, next_states, rewards, dones, weights=None):
"""
Train SAC
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is ... |
Train SAC
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
np.ndarray: TD error
"""
if isinstance(actions, t... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
np.ndarray: TD error
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def __init__(self,
action_dim,
obs_shape=(84, 84, 9),
n_conv_layers=4,
n_conv_filters=32,
feature_dim=50,
tau_encoder=0.05,
tau_critic=0.01,
auto_alpha=True,
lr_sac=1e... |
Initialize SAC+AE
Args:
action_dim (int):
obs_shape: (iterable of int): The default is ``(84, 84, 9)``
n_conv_layers (int): Number of convolutional layers at encoder. The default is ``4``
n_conv_filters (int): Number of filters in convolutional layers. T... | __init__ | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def get_action(self, state, test=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
Notes:
When the input i... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
Notes:
When the input image have different size, cropped image is used
... | get_action | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def train(self, states, actions, next_states, rewards, dones, weights=None):
"""
Train SAC+AE
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights ... |
Train SAC+AE
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def __init__(
self,
state_shape,
action_dim,
name="TD3",
actor_update_freq=2,
policy_noise=0.2,
noise_clip=0.5,
critic_units=(400, 300),
**kwargs):
"""
Initialize TD3
Args:
sh... |
Initialize TD3
Args:
shate_shape (iterable of ints): Observation state shape
action_dim (int): Action dimension
name (str): Network name. The default is ``"TD3"``.
actor_update_freq (int): Number of critic updates per one actor upate.
policy_... | __init__ | python | keiohta/tf2rl | tf2rl/algos/td3.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/td3.py | MIT |
def __init__(
self,
state_shape,
action_dim,
units=(32, 32),
n_latent_unit=32,
lr=5e-5,
kl_target=0.5,
reg_param=0.,
enable_sn=False,
enable_gp=False,
name="VAIL",
**kwargs):
... |
Initialize VAIL
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``5e-5``
kl_target (float): The default is ``0.5``
reg_param (fl... | __init__ | python | keiohta/tf2rl | tf2rl/algos/vail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vail.py | MIT |
def _compute_kl_latent(self, means, log_stds):
r"""
Compute KL divergence of latent spaces over standard Normal
distribution to compute loss in eq.5. The formulation of
KL divergence between two normal distributions is as follows:
ln(\sigma_2 / \sigma_1) + {(\mu_1 - \mu_2)^2... |
Compute KL divergence of latent spaces over standard Normal
distribution to compute loss in eq.5. The formulation of
KL divergence between two normal distributions is as follows:
ln(\sigma_2 / \sigma_1) + {(\mu_1 - \mu_2)^2 + \sigma_1^2 - \sigma_2^2} / (2 * \sigma_2^2)
Sinc... | _compute_kl_latent | python | keiohta/tf2rl | tf2rl/algos/vail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vail.py | MIT |
def __init__(
self,
state_shape,
action_dim,
is_discrete,
actor=None,
critic=None,
actor_critic=None,
max_action=1.,
actor_units=(256, 256),
critic_units=(256, 256),
lr_actor=1e-3,
... |
Initialize VPG
Args:
state_shape (iterable of int):
action_dim (int):
is_discrete (bool):
actor:
critic:
actor_critic:
max_action (float): maximum action size.
actor_units (iterable of int): Numbers of unit... | __init__ | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def get_action(self, state, test=False):
"""
Get action and probability
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray or float: Selected action
np.ndarray or f... |
Get action and probability
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray or float: Selected action
np.ndarray or float: Log(p)
| get_action | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def get_action_and_val(self, state, test=False):
"""
Get action, probability, and critic value
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray: Selected action
n... |
Get action, probability, and critic value
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray: Selected action
np.ndarray: Log(p)
np.ndarray: Critic value
... | get_action_and_val | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def train(self, states, actions, advantages, logp_olds, returns):
"""
Train VPG
Args:
states
actions
advantages
logp_olds
returns
"""
# Train actor and critic
actor_loss, logp_news = self._train_actor_body(
... |
Train VPG
Args:
states
actions
advantages
logp_olds
returns
| train | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def __init__(self, env, noop_max=30):
"""
Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
gym.Wrapper.__init__(self, env)
self.noop_max = noop_max
self.override_num_noops = None
self.noop_action = 0
... |
Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def reset(self, **kwargs):
"""
Do no-op action for a number of steps in [1, noop_max].
"""
self.env.reset(**kwargs)
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = self.unwrapped.np_random.randint(
... |
Do no-op action for a number of steps in [1, noop_max].
| reset | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env):
"""
Take action on reset for environments that are fixed until firing.
"""
gym.Wrapper.__init__(self, env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3 |
Take action on reset for environments that are fixed until firing.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env):
"""
Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
gym.Wrapper.__init__(self, env)
self.lives = 0
self.was_real_done = True |
Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def reset(self, **kwargs):
"""
Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset(**kwa... |
Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
| reset | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, skip=4):
"""
Return only every `skip`-th frame
"""
gym.Wrapper.__init__(self, env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = np.zeros(
(2,)+env.observation_space.shape, dtype=np.uint8)
... |
Return only every `skip`-th frame
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def step(self, action):
"""
Repeat action, sum reward, and max over last observations.
"""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_... |
Repeat action, sum reward, and max over last observations.
| step | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
"""
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
... |
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, k):
"""
Stack k last frames.
Returns lazy array, which is much more memory efficient.
See also baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp =... |
Stack k last frames.
Returns lazy array, which is much more memory efficient.
See also baselines.common.atari_wrappers.LazyFrames
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, frames):
"""
This object ensures that common frames between the observations are only stored once.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
buffers.
This object should only be converted to numpy array before being p... |
This object ensures that common frames between the observations are only stored once.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
buffers.
This object should only be converted to numpy array before being passed to the model.
You'd not b... | __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
"""
Configure environment for DeepMind-style Atari.
"""
if episode_life:
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFr... |
Configure environment for DeepMind-style Atari.
| wrap_deepmind | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
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