def train(self,
n_episodes,
noisy_policy_parameters=True,
goal=None,
**kwargs):
if goal is None:
str = "Training for {} episodes with random goals".format(
n_episodes)
else:
str = "Training for {} episodes on the goal {}".format(
n_episodes, goal)
print_info(str)
return (self._run(n_episodes,
train=True,
goal=goal,
noisy_policy_parameters=noisy_policy_parameters,
**kwargs))
def bootstrap(self,
n_bootstrap,
save_to_replay_buffer=False,
noisy_action=False,
render=False):
print_info("Bootstrapping: Motor babbling...")
if n_bootstrap <= 0:
raise (ValueError("0 bootstrap epoch selected, select at least 1"))
for epoch, m in enumerate(
self.environment.random_motors(n=n_bootstrap)):
s, _ = self.environment.compute_sensori_effect(
m,
save_to_replay_buffer=save_to_replay_buffer,
noisy_action=noisy_action,
render=render,
hooks=self.hooks)
print("Iteration {}/{}. Achievement: {}".format(
epoch + 1, n_bootstrap, s))
self.model.update(m, s)
def experiments(self, number, script_function, nb_processes=16):
"""Execute the given function in different subprocesses"""
print_info("Beginning {} experiments".format(number))
for experiment_count in range(1, number + 1):
print_info("Spinning experiment {}/{}".format(
experiment_count, number))
experiment_id = str(experiment_count)
experiment_id_full = (self.name + "/" + experiment_id)
experiment = Experiment(experiment_id=experiment_id_full,
experiments=self,
path=self._path,
hooks=[ExperimentArrayHook()])
# Run the experiment in a separate process
experiment_process = multiprocessing.Process(
name=experiment_id,
target=experiment_function,
args=(script_function, experiment))
experiment_process.start()