def run(workspace: str,
iterations: int,
render_training: bool, render_validation: bool, render_test: bool):
# Define the logger
logger: logging.Logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# Generate the Phy Environment
environment: PhyEnvironment = PhyEnvironment("phy_environment", urllc_param=None, rl_env_ver='bernoulli', vision_ahead=6,
cw_tot_number=120, cw_class=None, q_norm=0)
# Note: thresholds are high so we can perform training without worrying about reaching a certain goal
validation_threshold: float = 0.0
validation_std: float or None = None
test_threshold: float = 0.0
test_std: float or None = None
# Define Neural Network layers
nn_config: Config = Config()
nn_config.add_hidden_layer(LayerType.dense, [128, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()], layer_name="dense_1")
nn_config.add_hidden_layer(LayerType.dense, [64, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()], layer_name="dense_2")
nn_config.add_hidden_layer(LayerType.dense, [32, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()], layer_name="dense_3")
# Define model
inner_model: ProximalPolicyOptimization = _define_ppo_model(actor_config=nn_config, critic_config=nn_config)
# Define agent
ppo_agent: PPOAgent = _define_agent(inner_model)
# Define experiment
experiment: PhyExperiment = PhyExperiment("phy_experiment-6",
validation_threshold=validation_threshold, validation_std=validation_std,
test_threshold=test_threshold, test_std=test_std,
environment=environment, agent=ppo_agent)
# Define experiment data
saves_to_keep: int = 15
plots_dpi: int = 150
parallel: int = 10
training_episodes: int = 2000
validation_episodes: int = 100
training_validation_volleys: int = 30
test_episodes: int = 100
test_volleys: int = 10
episode_length_max: int = 140
# Run experiment
run_experiment(logger=logger, experiment=experiment,
file_name=__file__, workspace_path=workspace,
training_volleys_episodes=training_episodes, validation_volleys_episodes=validation_episodes,
training_validation_volleys=training_validation_volleys,
test_volleys_episodes=test_episodes, test_volleys=test_volleys,
episode_length=episode_length_max, parallel=parallel,
render_during_training=render_training, render_during_validation=render_validation,
render_during_test=render_test,
iterations=iterations, saves_to_keep=saves_to_keep, plots_dpi=plots_dpi)
def run(log_path: str, iteration: int, render: bool,
urllc_param: float, cw_tot_number: int, cw_classes: list,
csv_path: str = None, episodes: int = 1000):
# Define the logger
logger: logging.Logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# Generate the Phy Environment
# Note: formally this should be changed each time to be like the environment the model was trained in
# However it works anyway, so long that action and state spaces are the same
environment: PhyEnvironment = PhyEnvironment("phy_environment", rl_env_ver='bernoulli', render=render,
urllc_param=urllc_param, q_norm=0,
cw_tot_number=cw_tot_number, cw_class=cw_classes)
# Note: these values should be set according to the environment the model is tested in
validation_threshold: float = 0.0
validation_std: float or None = None
test_threshold: float = 0.0
test_std: float or None = None
# Define agent
aggressive_agent: AggressiveAgent = _define_agent()
# Define experiment
experiment: PhyExperiment = PhyExperiment("aggressive",
validation_threshold=validation_threshold, validation_std=validation_std,
test_threshold=test_threshold, test_std=test_std,
environment=environment, agent=aggressive_agent,
csv_path=csv_path)
# Define experiment data
# episodes: int = 100
volleys: int = 1
episode_length_max: int = 1400
if experiment.setup(logger=logger, iteration=iteration):
# Prepare the logger handlers
logger.handlers = []
# Generate a console and a file handler for the logger
console_handler: logging.StreamHandler = logging.StreamHandler(sys.stdout)
file_handler: logging.FileHandler = logging.FileHandler(log_path + "/info.log", "w+")
# Set handlers properties
console_handler.setLevel(logging.DEBUG)
file_handler.setLevel(logging.DEBUG)
formatter: logging.Formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
file_handler.setFormatter(formatter)
# Add the handlers to the logger
logger.addHandler(console_handler)
logger.addHandler(file_handler)
# Actually test the model
experiment.test(logger=logger, episodes=episodes, volleys=volleys, episode_length=episode_length_max,
restore_path="", render=render)
def run(restore_path: str,
log_path: str,
iteration: int,
render: bool,
urllc_param: float,
cw_tot_number: int,
cw_classes: list,
csv_path: str = None,
episodes: int = 1000):
# Define the logger
logger: logging.Logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# Generate the Phy Environment
# Note: formally this should be changed each time to be like the environment the model was trained in
# However it works anyway, so long that action and state spaces are the same
environment: PhyEnvironment = PhyEnvironment("phy_environment",
rl_env_ver='bernoulli',
render=render,
urllc_param=urllc_param,
q_norm=0,
cw_tot_number=cw_tot_number,
cw_class=cw_classes)
# Note: these values should be set according to the environment the model is tested in
validation_threshold: float = 0.0
validation_std: float or None = None
test_threshold: float = 0.0
test_std: float or None = None
# Define Neural Network layers
# Note: this is important, should be the same of the loaded model (the initializers are not relevant)
nn_config: Config = Config()
nn_config.add_hidden_layer(LayerType.dense, [
128, tensorflow.nn.relu, True,
tensorflow.contrib.layers.xavier_initializer()
],
layer_name="dense_1")
nn_config.add_hidden_layer(LayerType.dense, [
64, tensorflow.nn.relu, True,
tensorflow.contrib.layers.xavier_initializer()
],
layer_name="dense_2")
nn_config.add_hidden_layer(LayerType.dense, [
32, tensorflow.nn.relu, True,
tensorflow.contrib.layers.xavier_initializer()
],
layer_name="dense_3")
# Define model
inner_model: ProximalPolicyOptimization = _define_ppo_model(
actor_config=nn_config, critic_config=nn_config)
# Define agent
ppo_agent: PPOAgent = _define_agent(inner_model)
# Define experiment
experiment: PhyExperiment = PhyExperiment(
"phy_experiment",
validation_threshold=validation_threshold,
validation_std=validation_std,
test_threshold=test_threshold,
test_std=test_std,
environment=environment,
agent=ppo_agent,
csv_path=csv_path,
model=iteration)
# Define experiment data
# episodes: int = 100
volleys: int = 1
episode_length_max: int = 1400
if experiment.setup(logger=logger, iteration=iteration):
# Prepare the logger handlers
logger.handlers = []
# Generate a console and a file handler for the logger
console_handler: logging.StreamHandler = logging.StreamHandler(
sys.stdout)
file_handler: logging.FileHandler = logging.FileHandler(
log_path + "/info.log", "w+")
# Set handlers properties
console_handler.setLevel(logging.DEBUG)
file_handler.setLevel(logging.DEBUG)
formatter: logging.Formatter = logging.Formatter(
"%(asctime)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)
file_handler.setFormatter(formatter)
# Add the handlers to the logger
logger.addHandler(console_handler)
logger.addHandler(file_handler)
# Actually test the model
experiment.test(logger=logger,
episodes=episodes,
volleys=volleys,
episode_length=episode_length_max,
restore_path=restore_path,
render=render)