def bootstrap(self, expe, n, bootstap_range_div):
conf = make_configuration(
expe.ag.conf.m_centers - expe.ag.conf.m_ranges /
(2 * bootstap_range_div), expe.ag.conf.m_centers +
expe.ag.conf.m_ranges / (2 * bootstap_range_div),
expe.ag.conf.s_centers - expe.ag.conf.s_ranges /
(2 * bootstap_range_div), expe.ag.conf.s_centers +
expe.ag.conf.s_ranges / (2 * bootstap_range_div))
m_rand = rand_bounds(conf.m_bounds, n=n)
for m in m_rand:
m[-expe.ag.dmp.
n_dmps:] = expe.ag.dmp.default[:expe.ag.dmp.
n_dmps] + conf.m_ranges[
-expe.ag.dmp.n_dmps:] * randn(
expe.ag.dmp.n_dmps)
mov = expe.ag.motor_primitive(m)
s = expe.env.update(mov, log=True)
s = expe.ag.sensory_primitive(s)
expe.ag.sensorimotor_model.update(m, s)
expe.ag.emit('choice', array([nan] * len(expe.ag.expl_dims)))
expe.ag.emit('inference', m)
expe.ag.emit('movement', mov)
expe.ag.emit('perception', s)
expe._update_logs()
def __init__(self, mid, m_space, s_space, env_conf):
explo_noise = 0.05
self.conf = make_configuration(env_conf.m_mins[m_space],
env_conf.m_maxs[m_space],
array(list(env_conf.m_mins[m_space]) + list(env_conf.s_mins))[s_space],
array(list(env_conf.m_maxs[m_space]) + list(env_conf.s_maxs))[s_space])
self.im_dims = self.conf.s_dims
self.mid = mid
self.m_space = m_space
self.s_space = s_space
self.motor_babbling_n_iter = 10
self.s = None
self.last_interest = 0
im_cls, kwargs = (MiscRandomInterest, {
'competence_measure': competence_dist,
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'})
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = (NonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':explo_noise})
self.sm = sm_cls(self.conf, **kwargs)
Agent.__init__(self, self.conf, self.sm, self.im)
def get_params(n_bfs, starting_position, babbling_name, sm_name, im_name):
n_dmps = len(starting_position)
default = zeros(n_dmps * (n_bfs + 2))
default[:n_dmps] = starting_position
default[-n_dmps:] = starting_position
poppy_ag = {
'm_mins':
list([-600] * (n_dmps * n_bfs)) + list(default[:n_dmps] - 180.),
'm_maxs':
list([600] * (n_dmps * n_bfs)) + list(default[:n_dmps] + 180.),
's_mins': [-1., -0.7, -0.1],
's_maxs': [1., 0.7, 0.7]
}
poppy_ag_conf = make_configuration(**poppy_ag)
im_dims = poppy_ag_conf.m_dims if babbling_name == 'motor' else poppy_ag_conf.s_dims
im = InterestModel.from_configuration(poppy_ag_conf, im_dims, im_name)
sm_cls, kwargs = sms[sm_name]
sm = sm_cls(poppy_ag_conf, **kwargs)
used = array([False] * n_dmps + [True] * (n_dmps * n_bfs) +
[True] * n_dmps)
return {
'n_dmps': n_dmps,
'n_bfs': n_bfs,
'used': used,
'default': default,
'conf': poppy_ag_conf,
'sm': sm,
'im': im
}
def __init__(self,
mid,
m_space,
s_space,
env_conf,
explo_noise=0.,
normalize_interests=True,
context_mode=None):
self.conf = make_configuration(
env_conf.m_mins[m_space], env_conf.m_maxs[m_space],
array(list(env_conf.m_mins[m_space]) +
list(env_conf.s_mins))[s_space],
array(list(env_conf.m_maxs[m_space]) +
list(env_conf.s_maxs))[s_space])
self.im_dims = self.conf.s_dims
self.mid = mid
self.m_space = m_space
self.context_mode = context_mode
self.s_space = s_space
self.motor_babbling_n_iter = 0
self.s = None
self.last_interest = 0
if context_mode is not None:
im_cls, kwargs = (ContextRandomInterest, {
'win_size': 50,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local',
'context_mode': context_mode
})
else:
im_cls, kwargs = (MiscRandomInterest, {
'win_size': 50,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'
})
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = (DemonstrableNN, {
'fwd': 'NN',
'inv': 'NN',
'sigma_explo_ratio': explo_noise
})
self.sm = sm_cls(self.conf, **kwargs)
Agent.__init__(self,
self.conf,
self.sm,
self.im,
context_mode=self.context_mode)
def __init__(self,
config,
n_motor_babbling=0,
explo_noise=0.1,
normalize_interests=False):
self.config = config
self.n_motor_babbling = n_motor_babbling
self.explo_noise = explo_noise
self.normalize_interests = normalize_interests
self.conf = make_configuration(**config)
self.t = 0
self.modules = {}
self.chosen_modules = []
self.progresses_evolution = {}
self.interests_evolution = {}
self.mid_control = ""
# Define motor and sensory spaces:
m_ndims = self.conf.m_ndims # number of motor parameters
self.m_space = range(m_ndims)
self.c_dims = range(m_ndims, m_ndims + 2)
self.s_hand = range(m_ndims + 2, m_ndims + 32)
self.s_joystick_1 = range(m_ndims + 32, m_ndims + 52)
self.s_joystick_2 = range(m_ndims + 52, m_ndims + 72)
self.s_ergo = range(m_ndims + 72, m_ndims + 92)
self.s_ball = range(m_ndims + 92, m_ndims + 112)
self.s_light = range(m_ndims + 112, m_ndims + 122)
self.s_sound = range(m_ndims + 122, m_ndims + 132)
self.s_spaces = dict(s_hand=self.s_hand,
s_joystick_1=self.s_joystick_1,
s_joystick_2=self.s_joystick_2,
s_ergo=self.s_ergo,
s_ball=self.s_ball,
s_light=self.s_light,
s_sound=self.s_sound)
self.modules["mod"] = LearningModule(
"mod",
self.m_space,
self.c_dims + self.s_hand + self.s_joystick_1 + self.s_joystick_2 +
self.s_ergo + self.s_ball + self.s_light + self.s_sound,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
for mid in self.modules.keys():
self.progresses_evolution[mid] = []
self.interests_evolution[mid] = []
def __init__(self, config, mid):
self.config = config #global config
self.mconf = config.modules[mid] # module config
if mid[0:3] == 'mod':
self.mid = mid
else:
raise ValueError('Module name must begin with mod')
#bounds config
#print self.mconf['m'], self.mconf['s']
self.conf = make_configuration(self.config.agent.mins[self.mconf['m']],
self.config.agent.maxs[self.mconf['m']],
self.config.agent.mins[self.mconf['s']],
self.config.agent.maxs[self.mconf['s']])
#print self.mconf['m'], self.mconf['s'], self.conf
self.im_dims = self.conf.m_dims if self.mconf[
'babbling_name'] == 'motor' else self.conf.s_dims
# self.im = InterestModel.from_configuration(self.conf,
# self.im_dims,
# self.mconf['im_name'])
self.im_mode = self.mconf["im_mode"]
self.s = None
self.sp = None
self.snn = None
self.last_module_to_credit = -1
self.last_interest = 0
im_cls, kwargs = config.ims[self.mconf['im_name']]
kwargs['mode'] = self.im_mode
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = config.sms[self.mconf['sm_name']]
self.sm = sm_cls(self.conf, **kwargs)
#print self.mconf['s'], self.config.agent.s_dims
#self.s_filter = [self.config.agent.s_dims.index(sd) for sd in self.mconf['s']]
Agent.__init__(self, self.conf, self.sm, self.im)
if self.mconf['from_log'] is not None:
from_log_dir = self.mconf['from_log'][0]
with open(from_log_dir + '/{}'.format('log.pickle'), 'r') as f:
log = cPickle.load(f)
f.close()
from_log_mod = self.mconf['from_log'][1]
self.fast_forward_models(log, from_log_mod,
self.mconf['from_log'][2])
#self.controled_vars = set(self.mconf['m'])
self.overall_interest = 0
self.social_interest = 0
self.top_down_interest = 0
self.top_down_points = Queue.Queue()
self.own_interest = 0
def __init__(self, config, mid):
self.config = config #global config
self.mconf = config.modules[mid] # module config
if mid[0:3] == 'mod':
self.mid = mid
else:
raise ValueError('Module name must begin with mod')
#bounds config
#print self.mconf['m'], self.mconf['s']
self.conf = make_configuration(self.config.agent.mins[self.mconf['m']],
self.config.agent.maxs[self.mconf['m']],
self.config.agent.mins[self.mconf['s']],
self.config.agent.maxs[self.mconf['s']])
#print self.mconf['m'], self.mconf['s'], self.conf
self.im_dims = self.conf.m_dims if self.mconf['babbling_name'] == 'motor' else self.conf.s_dims
# self.im = InterestModel.from_configuration(self.conf,
# self.im_dims,
# self.mconf['im_name'])
self.im_mode = self.mconf["im_mode"]
self.s = None
self.sp = None
self.snn = None
self.last_module_to_credit = -1
self.last_interest = 0
im_cls, kwargs = config.ims[self.mconf['im_name']]
kwargs['mode'] = self.im_mode
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = config.sms[self.mconf['sm_name']]
self.sm = sm_cls(self.conf, **kwargs)
#print self.mconf['s'], self.config.agent.s_dims
#self.s_filter = [self.config.agent.s_dims.index(sd) for sd in self.mconf['s']]
Agent.__init__(self, self.conf, self.sm, self.im)
if self.mconf['from_log'] is not None:
from_log_dir = self.mconf['from_log'][0]
with open(from_log_dir + '/{}'.format('log.pickle'), 'r') as f:
log = cPickle.load(f)
f.close()
from_log_mod = self.mconf['from_log'][1]
self.fast_forward_models(log, from_log_mod, self.mconf['from_log'][2])
#self.controled_vars = set(self.mconf['m'])
self.overall_interest = 0
self.social_interest = 0
self.top_down_interest = 0
self.top_down_points = Queue.Queue()
self.own_interest = 0
def __init__(self, mid, m_space, s_space, env_conf, explo_noise, win_size, interest_model):
explo_noise = explo_noise
self.conf = make_configuration(env_conf.m_mins[m_space],
env_conf.m_maxs[m_space],
array(list(env_conf.m_mins[m_space]) + list(env_conf.s_mins))[s_space],
array(list(env_conf.m_maxs[m_space]) + list(env_conf.s_maxs))[s_space])
self.im_dims = self.conf.s_dims
self.mid = mid
self.m_space = m_space
self.s_space = s_space
self.motor_babbling_n_iter = 10
self.s = None
self.last_interest = 0
if interest_model == 'uniform':
im_cls, kwargs = (MiscRandomInterest, {
'competence_measure': competence_dist,
'win_size': win_size,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'})
elif interest_model == 'normal':
im_cls, kwargs = (MiscGaussianInterest, {
'competence_measure': competence_dist,
'win_size': win_size,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'})
elif interest_model == 'active':
im_cls, kwargs = (MiscDiscretizedInterest, {
'x_card': 20 ** len(self.im_dims), # 20 is the number of cells on each dimension
'cells_win_size': 20, # window size parameter (ws)
'eps_random': 0.1, # proportion of random choice of cell
'measure': competence_dist,
'competence_measure': competence_dist,
'win_size': win_size,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'})
else:
raise NotImplementedError
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = (NonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio': explo_noise})
self.sm = sm_cls(self.conf, **kwargs)
Agent.__init__(self, self.conf, self.sm, self.im)
def __init__(self,
mid,
m_space,
s_space,
env_conf,
explo_noise=0.,
normalize_interests=True,
context_mode=None):
self.conf = make_configuration(
env_conf.m_mins[m_space], env_conf.m_maxs[m_space],
array(list(env_conf.m_mins[m_space]) +
list(env_conf.s_mins))[s_space],
array(list(env_conf.m_maxs[m_space]) +
list(env_conf.s_maxs))[s_space])
self.im_dims = self.conf.s_dims
self.mid = mid
self.m_space = m_space
self.context_mode = context_mode
self.s_space = s_space
self.motor_babbling_n_iter = 0
self.n_mdims = 4
self.n_sdims = len(s_space) // 10
self.explo_noise = 0.2
self.s = None
self.last_interest = 0
if context_mode is None:
self.im = MiscRandomInterest(self.conf,
self.conf.s_dims,
self.n_sdims,
win_size=100)
else:
self.im = ContextRandomInterest(self.conf, self.conf.s_dims,
self.n_sdims, 100, context_mode)
#self.im = im_cls(self.conf, self.im_dims, **kwargs)
self.sm = BufferedDataset(self.conf.m_ndims,
self.conf.s_ndims,
buffer_size=10000,
lateness=10)
#sm_cls, kwargs = (DemonstrableNN, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':explo_noise})
#self.sm = sm_cls(self.conf, **kwargs)
Agent.__init__(self,
self.conf,
self.sm,
self.im,
context_mode=self.context_mode)
def __init__(self, conf,
model_type_head='WNN', config_str_head='default',
model_type_arm= 'WNN', config_str_arm ='default'):
"""NaoSensorimotorModel init method.
Args:
conf (NaoConfiguration): a configuration class object
model_type_head (str, optional)
config_str_head (str, optional)
model_type_arm (str, optional)
config_str_arm (str, optional)
"""
SensorimotorModel.__init__(self,conf)
conf_sm_model_head = make_configuration(
self.conf.mins[self.conf.head_dims],
self.conf.maxs[self.conf.head_dims],
self.conf.mins[self.conf.image_dims
+ self.conf.arm_dims],
self.conf.maxs[self.conf.image_dims
+ self.conf.arm_dims])
conf_sm_model_arm = make_configuration(
self.conf.mins[self.conf.arm_dims],
self.conf.maxs[self.conf.arm_dims],
self.conf.mins[self.conf.space_dims],
self.conf.maxs[self.conf.space_dims])
self.sm_model_head = \
SensorimotorModel.from_configuration(conf_sm_model_head,
model_type_head,
config_str_head)
self.sm_model_arm = \
SensorimotorModel.from_configuration(conf_sm_model_arm,
model_type_arm,
config_str_arm)
self.t = 0
self.mode = 'explore'
self.sm_model_head.mode = 'explore'
self.sm_model_arm.mode = 'explore'
self.sigma_m = [0.1,0.1]
def bootstrap(self, expe, n, bootstap_range_div):
conf = make_configuration(expe.ag.conf.m_centers - expe.ag.conf.m_ranges/(2 * bootstap_range_div),
expe.ag.conf.m_centers + expe.ag.conf.m_ranges/(2 * bootstap_range_div),
expe.ag.conf.s_centers - expe.ag.conf.s_ranges/(2 * bootstap_range_div),
expe.ag.conf.s_centers + expe.ag.conf.s_ranges/(2 * bootstap_range_div))
m_rand = rand_bounds(conf.m_bounds, n=n)
for m in m_rand:
m[-expe.ag.dmp.n_dmps:] = expe.ag.dmp.default[:expe.ag.dmp.n_dmps] + conf.m_ranges[-expe.ag.dmp.n_dmps:] * randn(expe.ag.dmp.n_dmps)
mov = expe.ag.motor_primitive(m)
s = expe.env.update(mov, log=True)
s = expe.ag.sensory_primitive(s)
expe.ag.sensorimotor_model.update(m, s)
expe.ag.emit('choice', array([nan] * len(expe.ag.expl_dims)))
expe.ag.emit('inference', m)
expe.ag.emit('movement', mov)
expe.ag.emit('perception', s)
expe._update_logs()
def __init__(self, mid, m_space, s_space, env_conf):
explo_noise = 0.05
self.conf = make_configuration(
env_conf.m_mins[m_space], env_conf.m_maxs[m_space],
array(list(env_conf.m_mins[m_space]) +
list(env_conf.s_mins))[s_space],
array(list(env_conf.m_maxs[m_space]) +
list(env_conf.s_maxs))[s_space])
self.im_dims = self.conf.s_dims
self.mid = mid
self.m_space = m_space
self.s_space = s_space
self.motor_babbling_n_iter = 10
self.s = None
self.last_interest = 0
im_cls, kwargs = (MiscRandomInterest, {
'competence_measure': competence_dist,
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'
})
self.im = im_cls(self.conf, self.im_dims, **kwargs)
sm_cls, kwargs = (NonParametric, {
'fwd': 'NN',
'inv': 'NN',
'sigma_explo_ratio': explo_noise
})
self.sm = sm_cls(self.conf, **kwargs)
Agent.__init__(self, self.conf, self.sm, self.im)
def __init__(self,
mid,
m_space,
s_space,
max_steps,
env_conf,
explo_noise=0.05,
motor_babbling_n_iter=10,
optim_explo=None,
end_point=False):
self.mid = mid
self.m_space = m_space
self.s_space = s_space
self.n_mdims = len(self.m_space)
self.n_sdims = len(self.s_space)
self.max_steps = max_steps
self.env_conf = env_conf
self.explo_noise = explo_noise
self.motor_babbling_n_iter = motor_babbling_n_iter
self.optim_explo = optim_explo
self.end_point = end_point
self.s = None
self.sg = None
self.last_interest = 0
self.t = 0
# Sensorimotor Model
conf = make_configuration(
list(env_conf.m_mins[m_space]) * self.max_steps,
list(env_conf.m_maxs[m_space]) * self.max_steps,
list(
np.array(
list(env_conf.m_mins[m_space]) +
list(env_conf.s_mins))[s_space]) * self.max_steps,
list(
np.array(
list(env_conf.m_maxs[m_space]) +
list(env_conf.s_maxs))[s_space]) * self.max_steps)
self.sm = BufferedDataset(
conf.m_ndims,
conf.s_ndims,
buffer_size=
10000, # Size of a small kdtree buffer to update this one often and move the data to the big kdtree less often
lateness=100
) # The model can be "late" by this number of points: they are not yet taken into account (added to the small kdtree)
if self.end_point:
self.sm_end = BufferedDataset(conf.m_ndims,
len(s_space),
buffer_size=10000,
lateness=100)
self.interest_model = MiscRandomInterest(
conf, conf.s_dims[-self.n_sdims:], self.n_sdims, win_size=200)
else:
self.interest_model = MiscRandomInterest(conf,
conf.s_dims,
self.n_sdims,
win_size=200)
def __init__(self,
name=None,
hierarchy_type=0,
babbling_name="goal",
supervisor_name="interest",
supervisor_explo="motor",
supervisor_n_explo_points=0,
supervisor_ccm="competence",
supervisor_ccl="local",
tdd=False,
ns=False,
perturbation=None,
from_log=None,
iterations=None):
################################### EXPERIMENT CONFIG ###################################
self.name = name or 'Experiment'
self.init_rest_trial = False
self.bootstrap = 100
self.bootstrap_range_div = 1.
self.iter = iterations or 50
self.log_each = self.iter #must be <= iter
self.eval_at = []
self.n_eval = 0
self.eval_modes = []
self.gui = False
self.hierarchy_type = hierarchy_type
self.babbling_name = babbling_name
if self.babbling_name == "goal":
self.motor_babbling_n_iter = 0
else:
self.motor_babbling_n_iter = self.iter
self.from_log = from_log
################################### AGENT CONFIG ###################################
self.n_dyn_motors = 4
self.n_dmps = self.n_dyn_motors
self.dmp_use_initial = False
self.dmp_use_goal = True
self.n_bfs = 2
self.n_static_motor = 0
self.rest_position = np.zeros(self.n_dmps + self.n_static_motor)
self.motor_n_dims = self.n_dyn_motors * self.n_bfs + self.n_static_motor
if self.dmp_use_initial:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
if self.dmp_use_goal:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
self.move_steps = 50
self.motor_dims = range(self.motor_n_dims)
if self.hierarchy_type <= 1:
self.s_n_dims = 5 * self.n_bfs + 5
elif self.hierarchy_type == 2:
self.s_n_dims = 7 * self.n_bfs + 5
else:
raise NotImplementedError
self.sensori_dims = range(self.motor_n_dims,
self.motor_n_dims + self.s_n_dims)
self.used_dims = self.motor_n_dims + self.s_n_dims
self.im_name = 'miscRandom_local'
self.choose_children_local = (supervisor_ccl == 'local')
self.sms = {
'knn1': (NonParametric, {
'fwd': 'NN',
'inv': 'NN',
'sigma_explo_ratio': 0.01
}),
}
sm = 'knn1'
im_mode = 'sg'
self.std_range = [-1., 1.]
m = self.motor_dims
s = self.sensori_dims
self.operators = ["par"]
if self.hierarchy_type == 0:
self.m_spaces = dict(m=m)
self.s_spaces = dict(s=s)
self.modules = dict(mod1=dict(
m=m,
s=s,
m_list=[m],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter), )
elif self.hierarchy_type == 1:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(s_h=range(self.motor_n_dims + 0,
self.motor_n_dims + 9),
s_t1=range(self.motor_n_dims + 9,
self.motor_n_dims + 15),
s_t2=range(self.motor_n_dims + 15,
self.motor_n_dims + 21),
s_o=range(self.motor_n_dims + 21,
self.motor_n_dims + 23),
s_b=range(self.motor_n_dims + 23,
self.motor_n_dims + 25))
self.modules = dict(
mod1=dict(m=self.m_spaces["m_arm"],
s=self.s_spaces["s_h"],
m_list=[self.m_spaces["m_arm"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod2=dict(m=self.s_spaces["s_h"],
s=self.s_spaces["s_t1"],
m_list=[self.s_spaces["s_h"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod3=dict(m=self.s_spaces["s_t1"],
s=self.s_spaces["s_o"],
m_list=[self.s_spaces["s_t1"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod4=dict(m=self.s_spaces["s_o"],
s=self.s_spaces["s_b"],
m_list=[self.s_spaces["s_o"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod5=dict(m=self.s_spaces["s_h"],
s=self.s_spaces["s_t2"],
m_list=[self.s_spaces["s_h"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod6=dict(m=self.s_spaces["s_t2"],
s=self.s_spaces["s_o"],
m_list=[self.s_spaces["s_t2"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
else:
raise NotImplementedError
self.supervisor_name = supervisor_name
self.supervisor_explo = supervisor_explo
self.supervisor_n_explo_points = supervisor_n_explo_points
self.supervisor_ccm = supervisor_ccm
self.supervisor_ccl = supervisor_ccl
if self.supervisor_name == "random":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="random",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="prop",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest_greedy":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="greedy",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest_bias":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="prop",
llb=True,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
else:
raise NotImplementedError
self.eval_dims = s[-4:-2]
self.eval_explo_dims = s[-4:-2]
self.eval_range = np.array([[-1.], [1.]])
self.eval_explo_eps = 0.02
self.eval_explo_comp_eps = 0.02
################################### Env CONFIG ###################################
self.max_param = 500. # max DMP weight
self.max_params = self.max_param * np.ones(
(self.n_dmps * self.n_bfs, ))
if self.dmp_use_initial:
self.max_params = np.append([1] * self.n_dmps, self.max_params)
if self.dmp_use_goal:
self.max_params = np.append(self.max_params, [1] * self.n_dmps)
self.env_cls = CogSci2016Environment
self.env_cfg = dict(move_steps=self.move_steps,
max_params=self.max_params,
perturbation=perturbation,
gui=self.gui)
self.rest_position = [0.] * self.motor_n_dims
self.m_mins = [-1.] * (self.n_dyn_motors * (self.n_bfs + 1))
self.m_maxs = [1.] * (self.n_dyn_motors * (self.n_bfs + 1))
self.s_mins = [-1.] * (3 * (self.n_bfs + 1)) + [-1.5] * (
self.n_bfs + 1) + [0.] * (self.n_bfs + 1) + [-1.5] * (
self.n_bfs + 1) + [0.] * (self.n_bfs + 1) + [-2., -2., 0., 0.]
self.s_maxs = [1.] * (3 * (self.n_bfs + 1)) + [1.5, 1.5] * (
self.n_bfs + 1) + [1.5, 1.5] * (self.n_bfs + 1) + [
2., 2., 10., 0.3
]
################################### Process CONFIG ###################################
self.agent = make_configuration(self.m_mins, self.m_maxs, self.s_mins,
self.s_maxs)
self.tag = self.name
self.log_dir = '' #determined later
def __init__(self,
config,
model_babbling="random",
n_motor_babbling=0,
explo_noise=0.1,
choice_eps=0.2,
proba_imitate=0.5,
tau=1.):
self.config = config
self.model_babbling = model_babbling
self.n_motor_babbling = n_motor_babbling
self.explo_noise = explo_noise
self.choice_eps = choice_eps
self.proba_imitate = proba_imitate
self.conf = make_configuration(**config)
self.t = 0
self.modules = {}
self.chosen_modules = []
self.goals = []
self.cp_evolution = {}
self.pp_evolution = {}
self.mid_control = None
self.last_cmd = None
# Define motor and sensory spaces:
m_ndims = self.conf.m_ndims # number of motor parameters
self.arm_n_dims = 40
self.diva_n_dims = 28
assert (m_ndims == self.arm_n_dims + self.diva_n_dims)
self.m_arm = range(self.arm_n_dims)
self.m_diva = range(self.arm_n_dims,
self.arm_n_dims + self.diva_n_dims)
self.m_space = range(m_ndims)
self.c_dims = range(m_ndims, m_ndims + 3)
self.s_hand = range(m_ndims + 3, m_ndims + 33)
self.s_culbuto_1 = range(m_ndims + 33, m_ndims + 63)
self.s_self_sound = range(m_ndims + 63, m_ndims + 73)
self.s_caregiver_sound = range(m_ndims + 73, m_ndims + 83)
self.s_spaces = dict(s_hand=self.s_hand,
s_culbuto_1=self.s_culbuto_1,
s_self_sound=self.s_self_sound,
s_caregiver_sound=self.s_caregiver_sound)
self.arm_modules = ['mod1', 'mod3', 'mod6']
self.diva_modules = ['mod12', 'mod14']
self.arm_goal_selection = 0.10
# Create the learning modules:
self.modules['mod1'] = LearningModule("mod1",
self.m_arm,
self.s_hand,
self.conf,
explo_noise=self.explo_noise)
self.modules['mod3'] = LearningModule(
"mod3",
self.m_arm,
self.c_dims + self.s_culbuto_1,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 2],
context_n_dims=3,
context_sensory_bounds=[[-2.] * 3, [2.] * 3]),
explo_noise=self.explo_noise)
self.modules['mod6'] = LearningModule(
"mod6",
self.m_arm,
self.c_dims + self.s_caregiver_sound,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 2],
context_n_dims=3,
context_sensory_bounds=[[-2.] * 3, [2.] * 3]),
explo_noise=self.explo_noise)
self.modules['mod12'] = LearningModule(
"mod12",
self.m_diva,
self.c_dims + self.s_culbuto_1,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 2],
context_n_dims=3,
context_sensory_bounds=[[-2.] * 3, [2.] * 3]),
explo_noise=self.explo_noise)
#self.modules['mod13'] = LearningModule("mod13", self.m_diva, self.s_self_sound, self.conf, explo_noise=self.explo_noise)
self.modules['mod14'] = LearningModule(
"mod14",
self.m_diva,
self.s_self_sound,
self.conf,
imitate="mod6",
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
for mid in self.modules.keys():
self.cp_evolution[mid] = []
self.pp_evolution[mid] = []
self.count_arm = 0
self.count_diva = 0
self.mids = [
"mod" + str(i) for i in range(1, 15)
if "mod" + str(i) in self.modules.keys()
]
#Book-keeping
self.actions = []
self.observations = []
def __init__(self,
config,
model_babbling="random",
n_motor_babbling=0,
explo_noise=0.1,
choice_eps=0.2,
proba_imitate=0.5):
self.config = config
self.model_babbling = model_babbling
self.n_motor_babbling = n_motor_babbling
self.explo_noise = explo_noise
self.choice_eps = choice_eps
self.proba_imitate = proba_imitate
self.conf = make_configuration(**config)
self.t = 0
self.modules = {}
self.chosen_modules = []
self.cp_evolution = {}
self.pp_evolution = {}
self.mid_control = None
self.last_cmd = None
# Define motor and sensory spaces:
m_ndims = self.conf.m_ndims # number of motor parameters
self.arm_n_dims = 21
self.diva_n_dims = 28
self.m_arm = range(self.arm_n_dims)
self.m_diva = range(self.arm_n_dims,
self.arm_n_dims + self.diva_n_dims)
self.m_space = range(m_ndims)
self.c_dims = range(m_ndims, m_ndims + 10)
self.s_hand = range(m_ndims + 10, m_ndims + 20)
self.s_tool = range(m_ndims + 20, m_ndims + 30)
self.s_toy1 = range(m_ndims + 30, m_ndims + 40)
self.s_toy2 = range(m_ndims + 40, m_ndims + 50)
self.s_toy3 = range(m_ndims + 50, m_ndims + 60)
self.s_sound = range(m_ndims + 60, m_ndims + 70)
self.s_caregiver = range(m_ndims + 70, m_ndims + 80)
self.s_spaces = dict(s_hand=self.s_hand,
s_tool=self.s_tool,
s_toy1=self.s_toy1,
s_toy2=self.s_toy2,
s_toy3=self.s_toy3,
s_sound=self.s_sound,
s_caregiver=self.s_caregiver)
# Create the 10 learning modules:
self.modules['mod1'] = LearningModule("mod1",
self.m_arm,
self.s_hand,
self.conf,
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
#self.modules['mod1'] = LearningModule("mod1", self.m_arm, self.c_dims + self.s_hand, self.conf, context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
self.modules['mod2'] = LearningModule(
"mod2",
self.m_arm,
self.c_dims[0:2] + self.s_tool,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1],
context_n_dims=2,
context_sensory_bounds=[[-1.] * 2, [1.] * 2]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod3'] = LearningModule(
"mod3",
self.m_arm,
self.c_dims[0:4] + self.s_toy1,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 2, 3],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod4'] = LearningModule(
"mod4",
self.m_arm,
self.c_dims[0:2] + self.c_dims[4:6] + self.s_toy2,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 4, 5],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod5'] = LearningModule(
"mod5",
self.m_arm,
self.c_dims[0:2] + self.c_dims[6:8] + self.s_toy3,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 6, 7],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod6'] = LearningModule(
"mod6",
self.m_arm,
self.c_dims[0:8] + self.s_sound,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[0, 1, 2, 3, 4, 5, 6, 7],
context_n_dims=8,
context_sensory_bounds=[[-1.] * 8, [1.] * 8]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod10'] = LearningModule(
"mod10",
self.m_diva,
self.c_dims[2:4] + self.c_dims[8:10] + self.s_toy1,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[2, 3, 8, 9],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod11'] = LearningModule(
"mod11",
self.m_diva,
self.c_dims[4:6] + self.c_dims[8:10] + self.s_toy2,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[4, 5, 8, 9],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
self.modules['mod12'] = LearningModule(
"mod12",
self.m_diva,
self.c_dims[6:8] + self.c_dims[8:10] + self.s_toy3,
self.conf,
context_mode=dict(mode='mcs',
context_dims=[6, 7, 8, 9],
context_n_dims=4,
context_sensory_bounds=[[-1.] * 4, [1.] * 4]),
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
#self.modules['mod13'] = LearningModule("mod13", self.m_diva, self.c_dims + self.s_sound, self.conf, imitate="mod6", context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
self.modules['mod13'] = LearningModule(
"mod13",
self.m_diva,
self.s_sound,
self.conf,
imitate="mod6",
explo_noise=self.explo_noise,
proba_imitate=self.proba_imitate)
#self.modules['mod7'] = LearningModule("mod7", self.m_arm, self.c_dims + self.s_caregiver, self.conf, context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
#self.modules['mod8'] = LearningModule("mod8", self.m_diva, self.c_dims + self.s_hand, self.conf, context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
#self.modules['mod9'] = LearningModule("mod9", self.m_diva, self.c_dims + self.s_tool, self.conf, context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
#self.modules['mod14'] = LearningModule("mod14", self.m_diva, self.c_dims + self.s_caregiver, self.conf, context_mode=dict(mode='mcs', context_n_dims=10, context_sensory_bounds=[[-1.]*10,[1.]*10]), explo_noise=self.explo_noise, proba_imitate=self.proba_imitate)
for mid in self.modules.keys():
self.cp_evolution[mid] = []
self.pp_evolution[mid] = []
self.count_arm = 0
self.count_diva = 0
self.mids = [
"mod" + str(i) for i in range(1, 15)
if "mod" + str(i) in self.modules.keys()
]
def __init__(self,
config,
babbling_mode="active",
n_motor_babbling=0,
explo_noise=0.1,
choice_eps=0.2,
normalize_interests=True):
self.config = config
self.babbling_mode = "prop" if babbling_mode == "active" else "random"
self.n_motor_babbling = n_motor_babbling
self.explo_noise = explo_noise
self.choice_eps = choice_eps,
self.normalize_interests = normalize_interests
self.conf = make_configuration(**config)
self.t = 0
self.modules = {}
self.chosen_modules = []
self.progresses_evolution = {}
self.interests_evolution = {}
self.have_to_replay_arm_demo = None
self.mid_control = ''
# Define motor and sensory spaces:
m_ndims = self.conf.m_ndims # number of motor parameters
self.m_space = range(m_ndims)
self.c_dims = range(m_ndims, m_ndims + 2)
self.s_hand = range(m_ndims + 2, m_ndims + 32)
self.s_joystick_1 = range(m_ndims + 32, m_ndims + 52)
self.s_joystick_2 = range(m_ndims + 52, m_ndims + 72)
self.s_ergo = range(m_ndims + 72, m_ndims + 92)
self.s_ball = range(m_ndims + 92, m_ndims + 112)
self.s_light = range(m_ndims + 112, m_ndims + 122)
self.s_sound = range(m_ndims + 122, m_ndims + 132)
self.s_spaces = dict(s_hand=self.s_hand,
s_joystick_1=self.s_joystick_1,
s_joystick_2=self.s_joystick_2,
s_ergo=self.s_ergo,
s_ball=self.s_ball,
s_light=self.s_light,
s_sound=self.s_sound)
#print
#print "Initialize agent with spaces:"
#print "Motor", self.m_space
#print "Context", self.c_dims
#print "Hand", self.s_hand
#print "Joystick1", self.s_joystick_1
#print "Joystick2", self.s_joystick_2
#print "Ergo", self.s_ergo
#print "Ball", self.s_ball
#print "Light", self.s_light
#print "Sound", self.s_sound
# Create the 6 learning modules:
self.modules['mod1'] = LearningModule(
"mod1",
self.m_space,
self.s_hand,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod2'] = LearningModule(
"mod2",
self.m_space,
self.s_joystick_1,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod3'] = LearningModule(
"mod3",
self.m_space,
self.s_joystick_2,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod4'] = LearningModule(
"mod4",
self.m_space, [self.c_dims[0]] + self.s_ergo,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=1,
context_sensory_bounds=[[-1.], [1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod5'] = LearningModule(
"mod5",
self.m_space,
self.c_dims + self.s_ball,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod6'] = LearningModule(
"mod6",
self.m_space,
self.c_dims + self.s_light,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod7'] = LearningModule(
"mod7",
self.m_space,
self.c_dims + self.s_sound,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.space2mid = dict(s_hand="mod1",
s_joystick_1="mod2",
s_joystick_2="mod3",
s_ergo="mod4",
s_ball="mod5",
s_light="mod6",
s_sound="mod7")
self.mid2space = dict(mod1="s_hand",
mod2="s_joystick_1",
mod3="s_joystick_2",
mod4="s_ergo",
mod5="s_ball",
mod6="s_light",
mod7="s_sound")
for mid in self.modules.keys():
self.progresses_evolution[mid] = []
self.interests_evolution[mid] = []
def __init__(self,
name=None,
hierarchy_type=0,
babbling_name="goal",
supervisor_name="interest",
supervisor_explo="motor",
supervisor_n_explo_points = 0,
supervisor_ccm="competence",
supervisor_ccl="local",
sm_model='NN',
im_model='miscRandom_local',
im_mode='sg',
tdd=False,
ns=False,
envnoise=0,
perturbation=None,
allow_split_mod1=False,
from_log=None,
bootstrap=0,
explo_noise=0.01,
iterations=None):
################################### EXPERIMENT CONFIG ###################################
self.name = name or 'Experiment'
self.init_rest_trial = False
self.bootstrap = bootstrap
self.bootstrap_range_div = 1.
self.iter = iterations or 50
self.log_each = self.iter #must be <= iter
self.eval_at = []
self.n_eval = 0
self.eval_modes = []
self.gui = False
self.hierarchy_type = hierarchy_type
self.babbling_name = babbling_name
if self.babbling_name == "goal":
self.motor_babbling_n_iter = 10
else:
self.motor_babbling_n_iter = self.iter
self.from_log = from_log
################################### AGENT CONFIG ###################################
self.n_dyn_motors = 4
self.n_dmps = self.n_dyn_motors
self.dmp_use_initial = False
self.dmp_use_goal = True
self.n_bfs = 2
self.n_static_motor = 0
self.rest_position = np.zeros(self.n_dmps + self.n_static_motor)
self.motor_n_dims = self.n_dyn_motors * self.n_bfs + self.n_static_motor
if self.dmp_use_initial:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
if self.dmp_use_goal:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
self.move_steps = 50
self.motor_dims = range(self.motor_n_dims)
self.s_n_dims = 31 * 3
self.sensori_dims = range(self.motor_n_dims, self.motor_n_dims + self.s_n_dims)
self.used_dims = self.motor_n_dims + self.s_n_dims
self.choose_children_local = (supervisor_ccl == 'local')
self.ims = {'miscRandom_local': (MiscRandomInterest, {
'competence_measure': competence_dist,
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'}),
'miscRandom_global': (MiscRandomInterest, {
'competence_measure': competence_dist,
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'global'}),
}
self.sms = {
'NN': (NonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':explo_noise}),
'LWLR-BFGS-EXPLO': (NonParametric, {'fwd': 'LWLR', 'k':10, 'sigma':0.1, 'sigma_explo_ratio':explo_noise, 'inv': 'L-BFGS-B', 'maxfun':200, 'ftol':0, 'gtol':0}),
'LWLR-BFGS-NOEXPLO': (NonParametric, {'fwd': 'LWLR', 'k':20, 'sigma':0.1, 'sigma_explo_ratio':0., 'inv': 'L-BFGS-B', 'maxfun':200, 'ftol':0, 'gtol':0}),
'LWLR-CMAES': (NonParametric, {'fwd': 'LWLR', 'k':10, 'sigma':0.1, 'inv': 'CMAES', 'cmaes_sigma':0.05, 'sigma_explo_ratio':explo_noise, 'maxfevals':20}),
}
self.sm_model = sm_model
self.im_model = im_model
self.im_name = self.im_model
sm = self.sm_model
self.std_range = [-1.,1.]
m = self.motor_dims
s = self.sensori_dims
self.operators = ["par"]
if self.hierarchy_type == 0:
self.m_spaces = dict(m=m)
self.s_spaces = dict(s=s)
self.modules = dict(mod1 = dict(m = m,
s = s,
m_list = [m],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
elif self.hierarchy_type == 1:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(s_h=range(self.motor_n_dims + 0, self.motor_n_dims + 9),
s_t1=range(self.motor_n_dims + 9, self.motor_n_dims + 15),
s_t2=range(self.motor_n_dims + 15, self.motor_n_dims + 21),
s_o1=range(self.motor_n_dims + 21, self.motor_n_dims + 27),
s_o2=range(self.motor_n_dims + 27, self.motor_n_dims + 33),
s_o3=range(self.motor_n_dims + 33, self.motor_n_dims + 39),
s_o4=range(self.motor_n_dims + 39, self.motor_n_dims + 45),
s_o5=range(self.motor_n_dims + 45, self.motor_n_dims + 51),
s_o6=range(self.motor_n_dims + 51, self.motor_n_dims + 57),
s_o7=range(self.motor_n_dims + 57, self.motor_n_dims + 63),
s_o8=range(self.motor_n_dims + 63, self.motor_n_dims + 69),
s_o9=range(self.motor_n_dims + 69, self.motor_n_dims + 75),
s_o10=range(self.motor_n_dims + 75, self.motor_n_dims + 81),
s_o11=range(self.motor_n_dims + 81, self.motor_n_dims + 87),
s_o12=range(self.motor_n_dims + 87, self.motor_n_dims + 93))
self.modules = dict(mod1 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_h"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod2 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_t1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod3 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_t2"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod4 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod5 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o2"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod6 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o3"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod7 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o4"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod8 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o5"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod9 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o6"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod10 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o7"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod11 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o8"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod12 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o9"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod13 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o10"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod14 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o11"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod15 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o12"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
elif self.hierarchy_type == 2:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(
s_o1=[self.motor_n_dims + 23, self.motor_n_dims + 26],#range(self.motor_n_dims + 21, self.motor_n_dims + 27),
s_o4=[self.motor_n_dims + 41, self.motor_n_dims + 44],#range(self.motor_n_dims + 39, self.motor_n_dims + 45),
)
self.modules = dict(
mod4 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod7 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o4"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
else:
raise NotImplementedError
self.supervisor_name = supervisor_name
self.supervisor_explo = supervisor_explo
self.supervisor_n_explo_points = supervisor_n_explo_points
self.supervisor_ccm = supervisor_ccm
self.supervisor_ccl = supervisor_ccl
if self.supervisor_name == "random":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="random",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest-pmin":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop-min",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest_greedy":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="greedy",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest_bias":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=True,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
else:
raise NotImplementedError
self.eval_dims = s[-4:-2]
self.eval_explo_dims = s[-4:-2]
self.eval_range = np.array([[-1.],
[1.]])
self.eval_explo_eps = 0.02
self.eval_explo_comp_eps = 0.02
################################### Env CONFIG ###################################
self.max_param = 500. # max DMP weight
self.max_params = self.max_param * np.ones((self.n_dmps * self.n_bfs,))
if self.dmp_use_initial:
self.max_params = np.append([1]*self.n_dmps, self.max_params)
if self.dmp_use_goal:
self.max_params = np.append(self.max_params, [1]*self.n_dmps)
self.env_cls = IROS2016Environment
self.env_cfg = dict(move_steps=self.move_steps,
max_params=self.max_params,
noise=envnoise,
perturbation=perturbation,
gui=self.gui)
self.rest_position = [0.] * self.motor_n_dims
self.m_mins = [-1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.m_maxs = [1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.s_mins = [-1.5] * 31 * 3
self.s_maxs = [1.5] * 31 * 3
################################### Process CONFIG ###################################
self.agent = make_configuration(self.m_mins,
self.m_maxs,
self.s_mins,
self.s_maxs)
self.tag = self.name
self.log_dir = ''#determined later
def __init__(self,
name=None,
hierarchy_type=0,
babbling_name="goal",
supervisor_name="interest",
supervisor_explo="motor",
supervisor_n_explo_points = 0,
supervisor_ccm="competence",
supervisor_ccl="local",
im_model='miscRandom_local',
tdd=False,
ns=False,
perturbation=None,
from_log=None,
iterations=None):
################################### EXPERIMENT CONFIG ###################################
self.name = name or 'Experiment'
self.init_rest_trial = False
self.bootstrap = 100
self.bootstrap_range_div = 1.
self.iter = iterations or 50
self.log_each = self.iter #must be <= iter
self.eval_at = []
self.n_eval = 0
self.eval_modes = []
self.gui = True
self.hierarchy_type = hierarchy_type
self.babbling_name = babbling_name
if self.babbling_name == "goal":
self.motor_babbling_n_iter = 0
else:
self.motor_babbling_n_iter = self.iter
self.from_log = from_log
################################### AGENT CONFIG ###################################
self.n_dyn_motors = 3
self.n_dmps = self.n_dyn_motors
self.dmp_use_initial = False
self.dmp_use_goal = True
self.n_bfs = 2
self.n_static_motor = 0
self.rest_position = np.zeros(self.n_dmps + self.n_static_motor)
self.motor_n_dims = self.n_dyn_motors * self.n_bfs + self.n_static_motor
if self.dmp_use_initial:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
if self.dmp_use_goal:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
self.n_context_dims = 2
self.context_mode = dict(mode='mcs',
reset_iterations=20,
context_n_dims=self.n_context_dims,
context_sensory_bounds=np.array([[-1.5, -1.5],[1.5, 1.5]]))
self.move_steps = 50
self.motor_dims = range(self.motor_n_dims)
self.s_n_dims = 17
self.sensori_dims = range(self.motor_n_dims, self.motor_n_dims + self.s_n_dims)
self.used_dims = self.motor_n_dims + self.s_n_dims
self.im_model = im_model
self.im_name = self.im_model
self.ims = {'miscRandom_local': (MiscRandomInterest, {
'competence_measure': competence_dist,
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'}),
'context_miscRandom_local': (ContextRandomInterest, {
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local',
'context_mode': self.context_mode}),
}
self.choose_children_local = (supervisor_ccl == 'local')
self.sms = {
'knn1': (NonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':0.01}),
'context_knn': (ContextNonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':0.01,'context_mode': self.context_mode}),
}
sm = 'knn1'
im_mode = 'sg'
self.std_range = [-1.,1.]
m = self.motor_dims
s = self.sensori_dims
self.operators = ["par"]
if self.hierarchy_type == 0:
self.m_spaces = dict(m=m)
self.s_spaces = dict(s=s)
self.modules = dict(mod1 = dict(m = m,
s = s,
m_list = [m],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
context_mode=self.context_mode,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
elif self.hierarchy_type == 1:
self.m_spaces = dict(m_arm=range(9))
self.s_spaces = dict(s_h=range(self.motor_n_dims + self.n_context_dims + 0, self.motor_n_dims + self.n_context_dims + 6),
s_t1=range(self.motor_n_dims + self.n_context_dims + 6, self.motor_n_dims + self.n_context_dims + 12),
#s_t2=range(self.motor_n_dims + self.n_context_dims + 15, self.motor_n_dims + self.n_context_dims + 21),
s_o=range(self.motor_n_dims, self.motor_n_dims + 2) + range(self.motor_n_dims + self.n_context_dims + 12, self.motor_n_dims + self.n_context_dims + 15))
self.modules = dict(mod1 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_h"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
context_mode=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod2 = dict(m = self.s_spaces["s_h"],
s = self.s_spaces["s_t1"],
m_list = [self.s_spaces["s_h"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
context_mode=None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
# mod3 = dict(m = self.s_spaces["s_h"],
# s = self.s_spaces["s_t2"],
# m_list = [self.s_spaces["s_h"]],
# operator = "par",
# babbling_name = "goal",
# sm_name = sm,
# im_name = self.im_name,
# im_mode = im_mode,
# from_log = None,
# context_mode=None,
# motor_babbling_n_iter=self.motor_babbling_n_iter),
mod3 = dict(m = self.s_spaces["s_h"],
s = self.s_spaces["s_o"],
m_list = [self.s_spaces["s_h"]],
operator = "par",
babbling_name = "goal",
sm_name = 'context_knn',
im_name = 'context_miscRandom_local',
im_mode = im_mode,
from_log = None,
context_mode=self.context_mode,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod4 = dict(m = self.s_spaces["s_t1"],
s = self.s_spaces["s_o"],
m_list = [self.s_spaces["s_t1"]],
operator = "par",
babbling_name = "goal",
sm_name = 'context_knn',
im_name = 'context_miscRandom_local',
im_mode = im_mode,
from_log = None,
context_mode=self.context_mode,
motor_babbling_n_iter=self.motor_babbling_n_iter),
# mod6 = dict(m = self.s_spaces["s_t2"],
# s = self.s_spaces["s_o"],
# m_list = [self.s_spaces["s_t2"]],
# operator = "par",
# babbling_name = "goal",
# sm_name = 'context_knn',
# im_name = 'context_miscRandom_local',
# im_mode = im_mode,
# from_log = None,
# context_mode=self.context_mode,
# motor_babbling_n_iter=self.motor_babbling_n_iter),
)
else:
raise NotImplementedError
self.supervisor_name = supervisor_name
self.supervisor_explo = supervisor_explo
self.supervisor_n_explo_points = supervisor_n_explo_points
self.supervisor_ccm = supervisor_ccm
self.supervisor_ccl = supervisor_ccl
if self.supervisor_name == "random":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="random",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest_greedy":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="greedy",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
elif self.supervisor_name == "interest_bias":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=True,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl)
else:
raise NotImplementedError
self.eval_dims = s[-4:-2]
self.eval_explo_dims = s[-4:-2]
self.eval_range = np.array([[-1.],
[1.]])
self.eval_explo_eps = 0.02
self.eval_explo_comp_eps = 0.02
################################### Env CONFIG ###################################
self.max_param = 300. # max DMP weight
self.max_params = self.max_param * np.ones((self.n_dmps * self.n_bfs,))
if self.dmp_use_initial:
self.max_params = np.append([1]*self.n_dmps, self.max_params)
if self.dmp_use_goal:
self.max_params = np.append(self.max_params, [1]*self.n_dmps)
iccm_conf = dict(move_steps=self.move_steps,
max_params=self.max_params,
gui=self.gui)
self.context_mode = dict(mode='mcs',
reset_iterations=10,
context_n_dims=2,
context_sensory_bounds=np.array([[-1.5, -1.5],[1.5, 1.5]]))
self.env_cls = ContextEnvironment
self.env_cfg = dict(env_cls=ICDL2016Environment,
env_conf=iccm_conf,
context_mode=self.context_mode)
self.rest_position = [0.] * self.motor_n_dims
self.m_mins = [-1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.m_maxs = [1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.s_mins = [-1.5] * (17)
self.s_maxs = [1.5] * (17)
################################### Process CONFIG ###################################
self.agent = make_configuration(self.m_mins,
self.m_maxs,
self.s_mins,
self.s_maxs)
self.tag = self.name
self.log_dir = ''#determined later
def __init__(self,
config,
babbling_mode="active",
n_motor_babbling=0.1,
explo_noise=0.05,
choice_eps=0.2,
normalize_interests=True):
self.config = config
self.babbling_mode = babbling_mode
self.n_motor_babbling = n_motor_babbling
self.explo_noise = explo_noise
self.choice_eps = choice_eps,
self.normalize_interests = normalize_interests
self.conf = make_configuration(**config)
self.t = 0
self.modules = {}
self.chosen_modules = []
self.goals = []
self.progresses_evolution = {}
self.interests_evolution = {}
self.ms = None
self.have_to_replay_arm_demo = None
self.mid_control = ''
self.measure_interest = False
# Define motor and sensory spaces:
m_ndims = self.conf.m_ndims # number of motor parameters
self.m_space = range(m_ndims)
self.c_dims = range(m_ndims, m_ndims + 2)
self.s_hand = range(m_ndims + 2, m_ndims + 32)
self.s_joystick_1 = range(m_ndims + 32, m_ndims + 52)
self.s_joystick_2 = range(m_ndims + 52, m_ndims + 72)
self.s_ergo = range(m_ndims + 72, m_ndims + 92)
self.s_ball = range(m_ndims + 92, m_ndims + 112)
self.s_light = range(m_ndims + 112, m_ndims + 122)
self.s_sound = range(m_ndims + 122, m_ndims + 132)
self.s_hand_right = range(m_ndims + 132, m_ndims + 162)
self.s_base = range(m_ndims + 162, m_ndims + 192)
self.s_arena = range(m_ndims + 192, m_ndims + 212)
self.s_obj1 = range(m_ndims + 212, m_ndims + 232)
self.s_obj2 = range(m_ndims + 232, m_ndims + 252)
self.s_obj3 = range(m_ndims + 252, m_ndims + 272)
self.s_rdm1 = range(m_ndims + 272, m_ndims + 292)
self.s_rdm2 = range(m_ndims + 292, m_ndims + 312)
self.s_spaces = dict(s_hand=self.s_hand,
s_joystick_1=self.s_joystick_1,
s_joystick_2=self.s_joystick_2,
s_ergo=self.s_ergo,
s_ball=self.s_ball,
s_light=self.s_light,
s_sound=self.s_sound,
s_hand_right=self.s_hand_right,
s_base=self.s_base,
s_arena=self.s_arena,
s_obj1=self.s_obj1,
s_obj2=self.s_obj2,
s_obj3=self.s_obj3,
s_rdm1=self.s_rdm1,
s_rdm2=self.s_rdm2)
#print
#print "Initialize agent with spaces:"
#print "Motor", self.m_space
#print "Context", self.c_dims
#print "Hand", self.s_hand
#print "Joystick1", self.s_joystick_1
#print "Joystick2", self.s_joystick_2
#print "Ergo", self.s_ergo
#print "Ball", self.s_ball
#print "Light", self.s_light
#print "Sound", self.s_sound
# Create the 6 learning modules:
self.modules['mod1'] = LearningModule(
"mod1",
self.m_space,
self.s_hand,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod2'] = LearningModule(
"mod2",
self.m_space,
self.s_joystick_1,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod3'] = LearningModule(
"mod3",
self.m_space,
self.s_joystick_2,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod4'] = LearningModule(
"mod4",
self.m_space, [self.c_dims[0]] + self.s_ergo,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=1,
context_sensory_bounds=[[-1.], [1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod5'] = LearningModule(
"mod5",
self.m_space,
self.c_dims + self.s_ball,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod6'] = LearningModule(
"mod6",
self.m_space,
self.c_dims + self.s_light,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod7'] = LearningModule(
"mod7",
self.m_space,
self.c_dims + self.s_sound,
self.conf,
context_mode=dict(mode='mcs',
context_n_dims=2,
context_sensory_bounds=[[-1., -1.], [1., 1.]]),
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod8'] = LearningModule(
"mod8",
self.m_space,
self.s_hand_right,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod9'] = LearningModule(
"mod9",
self.m_space,
self.s_base,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod10'] = LearningModule(
"mod10",
self.m_space,
self.s_arena,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod11'] = LearningModule(
"mod11",
self.m_space,
self.s_obj1,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod12'] = LearningModule(
"mod12",
self.m_space,
self.s_obj2,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod13'] = LearningModule(
"mod13",
self.m_space,
self.s_obj3,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod14'] = LearningModule(
"mod14",
self.m_space,
self.s_rdm1,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.modules['mod15'] = LearningModule(
"mod15",
self.m_space,
self.s_rdm2,
self.conf,
explo_noise=self.explo_noise,
normalize_interests=self.normalize_interests)
self.space2mid = dict(s_hand="mod1",
s_joystick_1="mod2",
s_joystick_2="mod3",
s_ergo="mod4",
s_ball="mod5",
s_light="mod6",
s_sound="mod7",
s_hand_right='mod8',
s_base='mod9',
s_arena='mod10',
s_obj1='mod11',
s_obj2='mod12',
s_obj3='mod13',
s_rdm1='mod14',
s_rdm2='mod15')
self.mid2space = dict(
mod1="s_hand",
mod2="s_joystick_1",
mod3="s_joystick_2",
mod4="s_ergo",
mod5="s_ball",
mod6="s_light",
mod7="s_sound",
mod8="s_hand_right",
mod9="s_base",
mod10="s_arena",
mod11="s_obj1",
mod12="s_obj2",
mod13="s_obj3",
mod14="s_rdm1",
mod15="s_rdm2",
)
for mid in self.modules.keys():
self.progresses_evolution[mid] = []
self.interests_evolution[mid] = []
def __init__(self,
name=None,
hierarchy_type=0,
babbling_name="goal",
supervisor_name="interest",
supervisor_explo="motor",
supervisor_n_explo_points = 0,
supervisor_ccm="competence",
supervisor_ccl="local",
sm_model='NN',
im_model='miscRandom_local',
im_mode='sg',
tdd=False,
ns=False,
envnoise=0,
perturbation=None,
allow_split_mod1=False,
from_log=None,
bootstrap=0,
explo_noise=0.01,
iterations=None):
################################### EXPERIMENT CONFIG ###################################
self.name = name or 'Experiment'
self.init_rest_trial = False
self.bootstrap = bootstrap
self.bootstrap_range_div = 1.
self.iter = iterations or 50
self.log_each = self.iter #must be <= iter
self.eval_at = []
self.n_eval = 0
self.eval_modes = []
self.gui = True
self.hierarchy_type = hierarchy_type
self.babbling_name = babbling_name
if self.babbling_name == "goal":
self.motor_babbling_n_iter = 10
else:
self.motor_babbling_n_iter = self.iter
self.from_log = from_log
################################### AGENT CONFIG ###################################
self.n_dyn_motors = 4
self.n_dmps = self.n_dyn_motors
self.dmp_use_initial = False
self.dmp_use_goal = True
self.n_bfs = 2
self.n_static_motor = 0
self.rest_position = np.zeros(self.n_dmps + self.n_static_motor)
self.motor_n_dims = self.n_dyn_motors * self.n_bfs + self.n_static_motor
if self.dmp_use_initial:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
if self.dmp_use_goal:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
self.move_steps = 50
self.motor_dims = range(self.motor_n_dims)
self.s_n_dims = 31 * 3
self.sensori_dims = range(self.motor_n_dims, self.motor_n_dims + self.s_n_dims)
self.used_dims = self.motor_n_dims + self.s_n_dims
self.choose_children_local = (supervisor_ccl == 'local')
self.ims = {'miscRandom_local': (MiscRandomInterest, {
'competence_measure': competence_dist,
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'local'}),
'miscRandom_global': (MiscRandomInterest, {
'competence_measure': competence_dist,
#'competence_measure': lambda target, reached, dist_max:competence_exp(target, reached, dist_min=0.01, dist_max=dist_max, power=20.),
'win_size': 1000,
'competence_mode': 'knn',
'k': 20,
'progress_mode': 'global'}),
}
self.sms = {
'NN': (NonParametric, {'fwd': 'NN', 'inv': 'NN', 'sigma_explo_ratio':explo_noise}),
'LWLR-BFGS-EXPLO': (NonParametric, {'fwd': 'LWLR', 'k':10, 'sigma':0.1, 'sigma_explo_ratio':explo_noise, 'inv': 'L-BFGS-B', 'maxfun':200, 'ftol':0, 'gtol':0}),
'LWLR-BFGS-NOEXPLO': (NonParametric, {'fwd': 'LWLR', 'k':20, 'sigma':0.1, 'sigma_explo_ratio':0., 'inv': 'L-BFGS-B', 'maxfun':200, 'ftol':0, 'gtol':0}),
'LWLR-CMAES': (NonParametric, {'fwd': 'LWLR', 'k':10, 'sigma':0.1, 'inv': 'CMAES', 'cmaes_sigma':0.05, 'sigma_explo_ratio':explo_noise, 'maxfevals':20}),
}
self.sm_model = sm_model
self.im_model = im_model
self.im_name = self.im_model
sm = self.sm_model
self.std_range = [-1.,1.]
m = self.motor_dims
s = self.sensori_dims
self.operators = ["par"]
if self.hierarchy_type == 0:
self.m_spaces = dict(m=m)
self.s_spaces = dict(s=s)
self.modules = dict(mod1 = dict(m = m,
s = s,
m_list = [m],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
elif self.hierarchy_type == 1:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(s_h=range(self.motor_n_dims + 0, self.motor_n_dims + 9),
s_t1=range(self.motor_n_dims + 9, self.motor_n_dims + 15),
s_t2=range(self.motor_n_dims + 15, self.motor_n_dims + 21),
s_o1=range(self.motor_n_dims + 21, self.motor_n_dims + 27),
s_o2=range(self.motor_n_dims + 27, self.motor_n_dims + 33),
s_o3=range(self.motor_n_dims + 33, self.motor_n_dims + 39),
s_o4=range(self.motor_n_dims + 39, self.motor_n_dims + 45),
s_o5=range(self.motor_n_dims + 45, self.motor_n_dims + 51),
s_o6=range(self.motor_n_dims + 51, self.motor_n_dims + 57),
s_o7=range(self.motor_n_dims + 57, self.motor_n_dims + 63),
s_o8=range(self.motor_n_dims + 63, self.motor_n_dims + 69),
s_o9=range(self.motor_n_dims + 69, self.motor_n_dims + 75),
s_o10=range(self.motor_n_dims + 75, self.motor_n_dims + 81),
s_o11=range(self.motor_n_dims + 81, self.motor_n_dims + 87),
s_o12=range(self.motor_n_dims + 87, self.motor_n_dims + 93))
self.modules = dict(mod1 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_h"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod2 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_t1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod3 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_t2"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod4 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod5 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o2"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod6 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o3"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod7 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o4"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod8 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o5"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod9 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o6"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod10 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o7"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod11 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o8"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod12 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o9"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod13 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o10"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod14 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o11"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod15 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o12"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
elif self.hierarchy_type == 2:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(
s_o1=[self.motor_n_dims + 23, self.motor_n_dims + 26],#range(self.motor_n_dims + 21, self.motor_n_dims + 27),
s_o4=[self.motor_n_dims + 41, self.motor_n_dims + 44],#range(self.motor_n_dims + 39, self.motor_n_dims + 45),
)
self.modules = dict(
mod4 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o1"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
mod7 = dict(m = self.m_spaces["m_arm"],
s = self.s_spaces["s_o4"],
m_list = [self.m_spaces["m_arm"]],
operator = "par",
babbling_name = "goal",
sm_name = sm,
im_name = self.im_name,
im_mode = im_mode,
from_log = None,
motor_babbling_n_iter=self.motor_babbling_n_iter),
)
else:
raise NotImplementedError
self.supervisor_name = supervisor_name
self.supervisor_explo = supervisor_explo
self.supervisor_n_explo_points = supervisor_n_explo_points
self.supervisor_ccm = supervisor_ccm
self.supervisor_ccl = supervisor_ccl
if self.supervisor_name == "random":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="random",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest-pmin":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop-min",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest_greedy":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="greedy",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
elif self.supervisor_name == "interest_bias":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(choice="prop",
llb=True,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
allow_split_mod1=allow_split_mod1)
else:
raise NotImplementedError
self.eval_dims = s[-4:-2]
self.eval_explo_dims = s[-4:-2]
self.eval_range = np.array([[-1.],
[1.]])
self.eval_explo_eps = 0.02
self.eval_explo_comp_eps = 0.02
################################### Env CONFIG ###################################
self.max_param = 500. # max DMP weight
self.max_params = self.max_param * np.ones((self.n_dmps * self.n_bfs,))
if self.dmp_use_initial:
self.max_params = np.append([1]*self.n_dmps, self.max_params)
if self.dmp_use_goal:
self.max_params = np.append(self.max_params, [1]*self.n_dmps)
self.env_cls = IROS2016Environment
self.env_cfg = dict(move_steps=self.move_steps,
max_params=self.max_params,
noise=envnoise,
perturbation=perturbation,
gui=self.gui)
self.rest_position = [0.] * self.motor_n_dims
self.m_mins = [-1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.m_maxs = [1.] * (self.n_dyn_motors * (self.n_bfs+1))
self.s_mins = [-1.5] * 31 * 3
self.s_maxs = [1.5] * 31 * 3
################################### Process CONFIG ###################################
self.agent = make_configuration(self.m_mins,
self.m_maxs,
self.s_mins,
self.s_maxs)
self.tag = self.name
self.log_dir = ''#determined later
def __init__(
self,
name=None,
hierarchy_type=0,
babbling_name="goal",
supervisor_name="interest",
supervisor_explo="motor",
supervisor_n_explo_points=0,
supervisor_ccm="competence",
supervisor_ccl="local",
tdd=False,
ns=False,
perturbation=None,
from_log=None,
iterations=None,
):
################################### EXPERIMENT CONFIG ###################################
self.name = name or "Experiment"
self.init_rest_trial = False
self.bootstrap = 100
self.bootstrap_range_div = 1.0
self.iter = iterations or 50
self.log_each = self.iter # must be <= iter
self.eval_at = []
self.n_eval = 0
self.eval_modes = []
self.gui = False
self.hierarchy_type = hierarchy_type
self.babbling_name = babbling_name
if self.babbling_name == "goal":
self.motor_babbling_n_iter = 0
else:
self.motor_babbling_n_iter = self.iter
self.from_log = from_log
################################### AGENT CONFIG ###################################
self.n_dyn_motors = 4
self.n_dmps = self.n_dyn_motors
self.dmp_use_initial = False
self.dmp_use_goal = True
self.n_bfs = 2
self.n_static_motor = 0
self.rest_position = np.zeros(self.n_dmps + self.n_static_motor)
self.motor_n_dims = self.n_dyn_motors * self.n_bfs + self.n_static_motor
if self.dmp_use_initial:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
if self.dmp_use_goal:
self.motor_n_dims = self.motor_n_dims + self.n_dmps
self.move_steps = 50
self.motor_dims = range(self.motor_n_dims)
if self.hierarchy_type <= 1:
self.s_n_dims = 5 * self.n_bfs + 5
elif self.hierarchy_type == 2:
self.s_n_dims = 7 * self.n_bfs + 5
else:
raise NotImplementedError
self.sensori_dims = range(self.motor_n_dims, self.motor_n_dims + self.s_n_dims)
self.used_dims = self.motor_n_dims + self.s_n_dims
self.im_name = "miscRandom_local"
self.choose_children_local = supervisor_ccl == "local"
self.sms = {"knn1": (NonParametric, {"fwd": "NN", "inv": "NN", "sigma_explo_ratio": 0.01})}
sm = "knn1"
im_mode = "sg"
self.std_range = [-1.0, 1.0]
m = self.motor_dims
s = self.sensori_dims
self.operators = ["par"]
if self.hierarchy_type == 0:
self.m_spaces = dict(m=m)
self.s_spaces = dict(s=s)
self.modules = dict(
mod1=dict(
m=m,
s=s,
m_list=[m],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
)
)
elif self.hierarchy_type == 1:
self.m_spaces = dict(m_arm=range(12))
self.s_spaces = dict(
s_h=range(self.motor_n_dims + 0, self.motor_n_dims + 9),
s_t1=range(self.motor_n_dims + 9, self.motor_n_dims + 15),
s_t2=range(self.motor_n_dims + 15, self.motor_n_dims + 21),
s_o=range(self.motor_n_dims + 21, self.motor_n_dims + 23),
s_b=range(self.motor_n_dims + 23, self.motor_n_dims + 25),
)
self.modules = dict(
mod1=dict(
m=self.m_spaces["m_arm"],
s=self.s_spaces["s_h"],
m_list=[self.m_spaces["m_arm"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
mod2=dict(
m=self.s_spaces["s_h"],
s=self.s_spaces["s_t1"],
m_list=[self.s_spaces["s_h"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
mod3=dict(
m=self.s_spaces["s_t1"],
s=self.s_spaces["s_o"],
m_list=[self.s_spaces["s_t1"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
mod4=dict(
m=self.s_spaces["s_o"],
s=self.s_spaces["s_b"],
m_list=[self.s_spaces["s_o"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
mod5=dict(
m=self.s_spaces["s_h"],
s=self.s_spaces["s_t2"],
m_list=[self.s_spaces["s_h"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
mod6=dict(
m=self.s_spaces["s_t2"],
s=self.s_spaces["s_o"],
m_list=[self.s_spaces["s_t2"]],
operator="par",
babbling_name="goal",
sm_name=sm,
im_name=self.im_name,
im_mode=im_mode,
from_log=None,
motor_babbling_n_iter=self.motor_babbling_n_iter,
),
)
else:
raise NotImplementedError
self.supervisor_name = supervisor_name
self.supervisor_explo = supervisor_explo
self.supervisor_n_explo_points = supervisor_n_explo_points
self.supervisor_ccm = supervisor_ccm
self.supervisor_ccl = supervisor_ccl
if self.supervisor_name == "random":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="random",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
)
elif self.supervisor_name == "interest":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="prop",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
)
elif self.supervisor_name == "interest_greedy":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="greedy",
llb=False,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
)
elif self.supervisor_name == "interest_bias":
self.supervisor_cls = Supervisor
self.supervisor_config = dict(
choice="prop",
llb=True,
explo=self.supervisor_explo,
n_explo_points=self.supervisor_n_explo_points,
choose_children_mode=self.supervisor_ccm,
choose_children_local=self.supervisor_ccl,
)
else:
raise NotImplementedError
self.eval_dims = s[-4:-2]
self.eval_explo_dims = s[-4:-2]
self.eval_range = np.array([[-1.0], [1.0]])
self.eval_explo_eps = 0.02
self.eval_explo_comp_eps = 0.02
################################### Env CONFIG ###################################
self.max_param = 500.0 # max DMP weight
self.max_params = self.max_param * np.ones((self.n_dmps * self.n_bfs,))
if self.dmp_use_initial:
self.max_params = np.append([1] * self.n_dmps, self.max_params)
if self.dmp_use_goal:
self.max_params = np.append(self.max_params, [1] * self.n_dmps)
self.env_cls = CogSci2016Environment
self.env_cfg = dict(
move_steps=self.move_steps, max_params=self.max_params, perturbation=perturbation, gui=self.gui
)
self.rest_position = [0.0] * self.motor_n_dims
self.m_mins = [-1.0] * (self.n_dyn_motors * (self.n_bfs + 1))
self.m_maxs = [1.0] * (self.n_dyn_motors * (self.n_bfs + 1))
self.s_mins = (
[-1.0] * (3 * (self.n_bfs + 1))
+ [-1.5] * (self.n_bfs + 1)
+ [0.0] * (self.n_bfs + 1)
+ [-1.5] * (self.n_bfs + 1)
+ [0.0] * (self.n_bfs + 1)
+ [-2.0, -2.0, 0.0, 0.0]
)
self.s_maxs = (
[1.0] * (3 * (self.n_bfs + 1))
+ [1.5, 1.5] * (self.n_bfs + 1)
+ [1.5, 1.5] * (self.n_bfs + 1)
+ [2.0, 2.0, 10.0, 0.3]
)
################################### Process CONFIG ###################################
self.agent = make_configuration(self.m_mins, self.m_maxs, self.s_mins, self.s_maxs)
self.tag = self.name
self.log_dir = "" # determined later
