Results = np.zeros((N_exp, 101))
for nu in np.linspace(0, 1, 101):
print("Experiments with nu={}".format(nu))
env_conf['nu'] = nu
for expr in range(N_exp):
objs = sample([i - 20 for i in range(10)], N_objs)
theta_d = [atan(obj / D) + pi / 2 for obj in objs]
env_conf['theta_d'] = theta_d
cg = CareGiver(env_conf)
arm = Arm(env_conf)
slide = []
itt = 1
while True:
arm.decided_wanted_obj()
action = arm.take_next_action()
R = cg.give_reward(action, arm.wanted_obj)
arm.update_inner_state(action, R, kernel='gaussian')
if len(slide) == M:
if np.mean(slide) > threshold:
Results[expr, int(nu * 100)] = itt
draw_world(objs, T, world)
cv.imshow(world_im_name, world)
cv.waitKey(int(1000*dt))
world = np.zeros((im_height,im_width,3), np.uint8)
self.q1 += (N_frames-int(N_frames))*omega1*dt
self.q2 += (N_frames-int(N_frames))*omega2*dt
self.draw(world)
draw_world(objs, T, world)
cv.imshow(world_im_name, world)
cv.waitKey(int(1000*dt))
### Setting agents:
env_config["nu"] = 0.3
cg = CareGiver(env_config)
robot = Robot(env_config)
mse = nn.MSELoss()
### Experiment and simulation:
# Phase one:
print("Phase one started")
running_loss = 0.0
for pitt in range(N_pitt):
a = np.zeros((N_anodes, N_anodes))
a[tuple(np.array(sample(range(N_anodes), 2)))] = 1
Results = {'Dirac':np.zeros((N_exp, N_itt - M)),\
'GaussianS':np.zeros((N_exp, N_itt - M)),\
'GaussianWS':np.zeros((N_exp, N_itt - M))}
for expr in range(N_exp):
objs = sample([i - 20 for i in range(10)], N_objs)
theta_d = [atan(obj / D) + pi / 2 for obj in objs]
env_config['theta_d'] = theta_d
# Dirac kernel :
print("Experiment - %s - %i" % ("Dirac kernel", expr))
env_config['nu'] = 0
cg = CareGiver(env_config)
arm = Arm(env_config)
slide = []
for itt in range(N_itt):
cg.T = 3 * pi / N_anodes
arm.decided_wanted_obj()
action = arm.take_next_action()
R, _ = cg.give_reward(action, arm.wanted_obj)
arm.update_inner_state(action, R, kernel=None)
if len(slide) == M:
Results['Dirac'][expr, itt - M] = np.mean(slide)
world = np.zeros((im_height, im_width, 3), np.uint8)
self.q1 += (N_frames - int(N_frames)) * omega1 * dt
self.q2 += (N_frames - int(N_frames)) * omega2 * dt
self.draw(world)
draw_world(objs, T, world)
cv.putText(world, data, (25, im_height - 25), cv.LINE_AA, 1,
(0, 0, 255), 2)
cv.imshow(world_im_name, world)
cv.waitKey(int(1000 * dt))
out.write(world)
### Setting agents:
cg = CareGiver(env_config)
robot = Robot(env_config)
### Experiment and simulation:
slide = []
R_moy = "None"
for itt in range(N_itt):
cg.T = 0.1
robot.decided_wanted_obj()
action = robot.take_next_action()
R, d = cg.give_reward(action, robot.wanted_obj)
robot.update_inner_state(action, R, kernel="gaussian")
pitt, N_pitt, loss_0))
else:
print('Pointing learning loss {}/{}: {}%'.format(
pitt, N_pitt,
100 * (running_loss / batch_size - loss_0) / loss_0))
running_loss = 0.0
eaa.update_retina_to_action_mapper()
slide = []
for itt in range(N_itt):
theta_d = [atan((obj - des) / D) + pi / 2 for obj in objs]
env_config["theta_d"] = theta_d
cg = CareGiver(env_config)
abstract_retina = np.array([[obj, objs[obj]] for obj in range(N_objs)])
eaa.decided_wanted_obj()
action = eaa.take_next_action()
R, _ = cg.give_reward(action, eaa.wanted_obj)
eaa.update_inner_state(action, R, abstract_retina)
if len(slide) == M:
Results['GaussianS'][expr, itt - M] = np.mean(slide)
slide.pop(0)
slide.append(R)
objs[0] += dx
# Without scaffolding :
for N_objs in range(1, N_objs_max + 1):
print("Experiments with {} objects".format(N_objs))
env_conf['N_objs'] = N_objs
for expr in range(N_exp):
# Dirac kernel :
objs = sample([i - 2 * N_objs_max for i in range(N_objs_max)], N_objs)
theta_d = [atan(obj / D) + pi / 2 for obj in objs]
env_conf['theta_d'] = theta_d
env_conf['nu'] = 0
cg = CareGiver(env_conf)
arm = Arm(env_conf)
slide = []
itt = 1
while True:
cg.T = 3 * pi / N_nodes
arm.decided_wanted_obj()
action = arm.take_next_action()
R = cg.give_reward(action, arm.wanted_obj)
arm.update_inner_state(action, R, kernel=None)
if len(slide) == M:
loss_0 = loss.item()
print('Pointing learning loss {}/{}: {}'.format(
pitt, N_pitt, loss.item()))
else:
print('Pointing learning loss {}/{}: {}%'.format(
pitt, N_pitt, 100 * (loss.item() - loss_0) / loss_0))
eaa.update_retina_to_action_mapper()
slide = []
for itt in range(N_itt):
theta_d = [atan((obj - des) / D) + pi / 2 for obj in objs]
env_config["theta_d"] = theta_d
cg = CareGiver(env_config)
abstract_retina = np.array([[obj, objs[obj]] for obj in range(N_objs)])
eaa.decided_wanted_obj()
action = eaa.take_next_action()
R = cg.give_reward(action, eaa.wanted_obj)
eaa.update_inner_state(action, R, abstract_retina)
if len(slide) == M:
Results['GaussianS'][expr, itt - M] = np.mean(slide)
slide.pop(0)
slide.append(R)
objs[0] += dx
''' # Without scaffolding :