def run(self):
date = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
log_dir = 'logs/' + date
os.mkdir(log_dir)
env = VrepEnvironment(self.robot, **conf)
ag = DmpAgent(**get_params(self.n_bfs, env.rest_position, self.babbling_name, self.sm_name, self.im_name))
print 'Running xp', self.tag
xp = Experiment(env, ag)
env.unsubscribe('motor', xp)
env.unsubscribe('sensori', xp)
ag.subscribe('movement', xp)
# xp.evaluate_at(eval_at, tc)
xp.log.env_conf = conf
xp.log.ag_conf = {'n_bfs': self.n_bfs,
'starting_position': env.rest_position,
'babbling_name': self.babbling_name,
'sm_name': self.sm_name,
'im_name': self.im_name
}
xp.log.bootstrap_conf = {'n': 16, 'bootstap_range_div': 28.}
self.bootstrap(xp, **xp.log.bootstrap_conf)
log_each = 10
for run in range(100 / log_each):
xp.run(log_each)
with open(log_dir + '/{}'.format(self.tag), 'wb') as f:
pickle.dump(xp.log, f)
f.close()
print 'saved ' + str((run + 1) * log_each)
class LearningIkXp(VrepXp):
def __init__(self, conf):
VrepXp.__init__(self, 'poppy', conf['scene'])
self.xp_conf = conf
def run(self):
env = VrepEnvironment(self.robot, **env_conf)
# Create the Interest Model
im_dims = env.conf.m_dims if self.xp_conf['bab'] == 'motor' else env.conf.s_dims
im_cls, im_conf = interest_models[self.xp_conf['im']['name']]
im_conf = im_conf['default']
im_conf.update(self.xp_conf['im']['conf'])
print 'Create IM', self.xp_conf['im']['name'], 'using', im_conf
im = im_cls(env.conf, im_dims, **im_conf)
# Create the SensoriMotor Model
sm_cls = sms[self.xp_conf['sm']['name']]
print 'Create SM', self.xp_conf['sm']['name'], 'using', self.xp_conf['sm']['conf']
sm = sm_cls(env.conf, **self.xp_conf['sm']['conf'])
ag = Agent(env.conf, sm, im)
self.xp = Experiment(env, ag)
self.xp.evaluate_at(self.xp_conf['eval_at'], load(self.xp_conf['tc']))
self.xp.run()
def save(self, f):
pickle.dump(self.xp.log, f)
def __init__(self, config, log=None, log_dir=None, n_trials=1):
self.config = config
if hasattr(config, 'env_cls') and hasattr(config, 'env_cfg'):
self.env = config.env_cls(**config.env_cfg)
else:
raise NotImplementedError
#self.env = VrepDivaEnvironment(self.config.environment, self.config.vrep, self.config.diva)
#self.ag = DmpAgent(self.config, self.env)
self.ag = self.config.supervisor_cls(self.config, self.env,
**self.config.supervisor_config)
Experiment.__init__(self, self.env, self.ag)
self.ag.log = self.log
if log is None:
if log_dir is None:
self.log_dir = (
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../../logs/') +
datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") +
'-' + config.tag)
else:
self.log_dir = log_dir + config.tag
try: # muliprocess collisions
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
except OSError:
pass
config.log_dir = self.log_dir
else:
assert log_dir is not None
self.log_dir = log_dir
self.log = log
self.ag.subscribe('agentM', self)
self.ag.subscribe('agentS', self)
self.ag.subscribe('interests', self)
self.ag.subscribe('babbling_module', self)
self.ag.subscribe('module_to_credit', self)
self.ag.subscribe('babbling_interest', self)
#self.ag.subscribe_topics_mod(['competence', 'chidren_choice'], self)
self.n_trials = n_trials
self.trial = 0
def __init__(self, config, log = None, log_dir = None, n_trials = 1):
self.config = config
if hasattr(config, 'env_cls') and hasattr(config, 'env_cfg'):
self.env = config.env_cls(**config.env_cfg)
else:
raise NotImplementedError
#self.env = VrepDivaEnvironment(self.config.environment, self.config.vrep, self.config.diva)
#self.ag = DmpAgent(self.config, self.env)
self.ag = self.config.supervisor_cls(self.config, self.env, **self.config.supervisor_config)
Experiment.__init__(self, self.env, self.ag)
self.ag.log = self.log
if log is None:
if log_dir is None:
self.log_dir = (os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../../logs/')
+ datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+ '-'
+ config.tag)
else:
self.log_dir = log_dir + config.tag
try: # muliprocess collisions
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
except OSError:
pass
config.log_dir = self.log_dir
else:
assert log_dir is not None
self.log_dir = log_dir
self.log = log
self.ag.subscribe('agentM', self)
self.ag.subscribe('agentS', self)
self.ag.subscribe('interests', self)
self.ag.subscribe('babbling_module', self)
self.ag.subscribe('module_to_credit', self)
self.ag.subscribe('babbling_interest', self)
#self.ag.subscribe_topics_mod(['competence', 'chidren_choice'], self)
self.n_trials = n_trials
self.trial = 0
def run(self):
date = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
log_dir = 'logs/' + date
os.mkdir(log_dir)
env = VrepEnvironment(self.robot, **conf)
ag = DmpAgent(**get_params(self.n_bfs, env.rest_position, self.
babbling_name, self.sm_name, self.im_name))
print 'Running xp', self.tag
xp = Experiment(env, ag)
env.unsubscribe('motor', xp)
env.unsubscribe('sensori', xp)
ag.subscribe('movement', xp)
# xp.evaluate_at(eval_at, tc)
xp.log.env_conf = conf
xp.log.ag_conf = {
'n_bfs': self.n_bfs,
'starting_position': env.rest_position,
'babbling_name': self.babbling_name,
'sm_name': self.sm_name,
'im_name': self.im_name
}
xp.log.bootstrap_conf = {'n': 16, 'bootstap_range_div': 28.}
self.bootstrap(xp, **xp.log.bootstrap_conf)
log_each = 10
for run in range(100 / log_each):
xp.run(log_each)
with open(log_dir + '/{}'.format(self.tag), 'wb') as f:
pickle.dump(xp.log, f)
f.close()
print 'saved ' + str((run + 1) * log_each)
def run(self):
env = VrepEnvironment(self.robot, **env_conf)
# Create the Interest Model
im_dims = env.conf.m_dims if self.xp_conf['bab'] == 'motor' else env.conf.s_dims
im_cls, im_conf = interest_models[self.xp_conf['im']['name']]
im_conf = im_conf['default']
im_conf.update(self.xp_conf['im']['conf'])
print 'Create IM', self.xp_conf['im']['name'], 'using', im_conf
im = im_cls(env.conf, im_dims, **im_conf)
# Create the SensoriMotor Model
sm_cls = sms[self.xp_conf['sm']['name']]
print 'Create SM', self.xp_conf['sm']['name'], 'using', self.xp_conf['sm']['conf']
sm = sm_cls(env.conf, **self.xp_conf['sm']['conf'])
ag = Agent(env.conf, sm, im)
self.xp = Experiment(env, ag)
self.xp.evaluate_at(self.xp_conf['eval_at'], load(self.xp_conf['tc']))
self.xp.run()
# To switch from motor to goal babbling
# You just need to change the babling parameter at instanciation
from explauto.experiment import Experiment, make_settings
s_goal = make_settings(environment='simple_arm',
babbling_mode='goal',
interest_model='random',
sensorimotor_model='nearest_neighbor')
goal_expe = Experiment.from_settings(s_goal)
goal_expe.evaluate_at([1, 10, 20, 30, 100, 200, 300, 400], s_goal.default_testcases)
goal_expe.run()
ax = axes()
data = goal_expe.log.scatter_plot(ax, (('sensori', [0, 1]), ), color='green')
# To switch from motor to goal babbling
# You just need to change the babling parameter at instanciation
from explauto.experiment import Experiment, make_settings
s_goal = make_settings(environment='simple_arm',
babbling_mode='goal',
interest_model='random',
sensorimotor_model='nearest_neighbor')
goal_expe = Experiment.from_settings(s_goal)
goal_expe.evaluate_at([1, 10, 20, 30, 100, 200, 300, 400],
s_goal.default_testcases)
goal_expe.run()
ax = axes()
data = goal_expe.log.scatter_plot(ax, (('sensori', [0, 1]), ), color='green')