def main():
name = 'LTL-MDP-GS'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
freeze_input=True,
multiproc=True,
use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Innerloop simulator
optimizee = StateActionOptimizee(traj)
# NOTE: Outerloop optimizer initialization
n_grid_divs_per_axis = 50
parameters = GridSearchParameters(
param_grid={
'gamma': (optimizee.bound[0], optimizee.bound[1],
n_grid_divs_per_axis),
#'lam': (optimizee.bound[0], optimizee.bound[1], n_grid_divs_per_axis),
'eta': (optimizee.bound[0], optimizee.bound[1],
n_grid_divs_per_axis),
})
optimizer = GridSearchOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.),
parameters=parameters)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-CE_6_8_TD1_New'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
freeze_input=True,
multiproc=True,
use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Benchmark function
optimizee = StateActionOptimizee(traj)
# NOTE: Outerloop optimizer initialization
# TODO: Change the optimizer to the appropriate Optimizer class
parameters = CrossEntropyParameters(pop_size=75,
rho=0.2,
smoothing=0.0,
temp_decay=0,
n_iteration=75,
distribution=NoisyGaussian(
noise_magnitude=1,
noise_decay=0.95),
stop_criterion=np.inf,
seed=102)
optimizer = CrossEntropyOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1., ),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Add Recorder
recorder = Recorder(trajectory=traj,
optimizee_name='SNN StateAction',
optimizee_parameters=['gamma', 'eta'],
optimizer_name=optimizer.__class__.__name__,
optimizer_parameters=optimizer.get_params())
recorder.start()
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
recorder.end()
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-GD_6_8_TD1_New'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title='{} data'.format(name),
comment='{} data'.format(name),
add_time=True,
freeze_input=True,
multiproc=True,
use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Benchmark function
optimizee = StateActionOptimizee(traj)
## Outerloop optimizer initialization
parameters = ClassicGDParameters(learning_rate=0.0001,
exploration_step_size=0.0001,
n_random_steps=75,
n_iteration=75,
stop_criterion=np.Inf,
seed=1234)
#parameters = AdamParameters(learning_rate=0.001, exploration_step_size=0.001, n_random_steps=75, first_order_decay=0.8,
# second_order_decay=0.8, n_iteration=75, stop_criterion=np.Inf, seed=99)
# parameters = StochasticGDParameters(learning_rate=0.01, stochastic_deviation=1, stochastic_decay=0.99,
# exploration_step_size=0.01, n_random_steps=5, n_iteration=100,
# stop_criterion=np.Inf)
#parameters = RMSPropParameters(learning_rate=0.01, exploration_step_size=0.01,
# n_random_steps=5, momentum_decay=0.5,
# n_iteration=100, stop_criterion=np.Inf, seed=99)
optimizer = GradientDescentOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1., ),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func,
base_point_evaluations=10)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Add Recorder
recorder = Recorder(trajectory=traj,
optimizee_name='SNN StateAction',
optimizee_parameters=['gamma', 'eta'],
optimizer_name=optimizer.__class__.__name__,
optimizer_parameters=optimizer.get_params())
recorder.start()
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
## Outerloop optimizer end
optimizer.end(traj)
recorder.end()
# Finally disable logging and close all log-files
env.disable_logging()