class Cluster:
"""
Computes optimal process parameters, at each layer, given feedback obtained
from the machine sensors.
Arguments:
shared_cfg (dotmap):
- **env.n_parts** (*int*): Total number of parts built under feedback control.
- **env.horizon** (*int*): Markov Decision Process horizon (here number of layers).
- **env.nS** (*int*): Dimension of the state vector.
- **comms** (*dotmap*): Parameters for communication with other classes.
pretrained_cfg (dotmap):
- **n_parts** (*dotmap*): Number of parts built under this control scheme.
- **ctrl_cfg** (*dotmap*): Configuration parameters passed to the MPC class.
learned_cfg (dotmap):
- **n_parts** (*dotmap*): Number of parts built under this control scheme.
- **ctrl_cfg** (*dotmap*): Configuration parameters passed to the MPC class.
"""
def __init__(self, shared_cfg, pretrained_cfg, learned_cfg):
self.s_cfg = shared_cfg
self.c_pre_cfg = pretrained_cfg
self.c_ler_cfg = learned_cfg
self.policyPret = MPC(pretrained_cfg.ctrl_cfg)
self.policyLear = MPC(learned_cfg.ctrl_cfg)
self.t = 0
self.H = shared_cfg.env.horizon
self.train_freq = learned_cfg.train_freq
self.n_parts = shared_cfg.env.n_parts
self.n_parts_pretrained = pretrained_cfg.n_parts
self.n_parts_learned = learned_cfg.n_parts
assert self.n_parts_pretrained + self.n_parts_learned == self.n_parts
self.pret_state_traj = np.zeros(
(self.n_parts_pretrained, self.H, shared_cfg.env.nS))
self.pret_action_traj = np.zeros((self.n_parts_pretrained, self.H, 2))
self.lear_state_traj = np.zeros(
(self.n_parts_learned, self.H, shared_cfg.env.nS))
self.lear_action_traj = np.zeros((self.n_parts_learned, self.H, 2))
self.pred_cost_pret = np.zeros((self.H, self.n_parts_pretrained))
self.pred_cost_lear = np.zeros((self.H, self.n_parts_learned))
self.save_dirs = [shared_cfg.save_dir1, shared_cfg.save_dir2]
self.clearComms()
# --------------------------------------------------------------------------
# COMMS FUNCTIONS
# --------------------------------------------------------------------------
def clearComms(self):
cfg = self.s_cfg.comms
dir_action = cfg.dir + cfg.action.rdy_name
dir_state = cfg.dir + cfg.state.rdy_name
if os.path.isdir(dir_action): os.rmdir(dir_action)
if os.path.isdir(dir_state): os.rmdir(dir_state)
def getStates(self):
"""Load state vectors uploaded to the server by the `Machine` class.
This function waits for the `comms.dir/comms.state.rdy_name` folder to be
created by the `Machine` class, before reading the file where the states
are located, `comms.dir/comms.state.f_name`
Returns:
np.array: State vector with shape (`n_parts`, `nS`)
"""
print('Waiting for states...')
dir = self.s_cfg.comms.dir
cfg = self.s_cfg.comms.state
rdy = dir + cfg.rdy_name
# Wait until RDY signal is provided
while (not os.path.isdir(rdy)):
pass
os.rmdir(rdy) # Delete RDY
# Read data to array
states = np.load(dir + cfg.f_name)
print('States received')
return states
def sendAction(self, actions):
"""Saves the computed actions.
Signals the `Machine` class that actions are ready to be downloaded by
locally creating the `comms.dir/comms.action.rdy_name` folder
Arguments:
actions (np.array): Action vector with shape (`n_parts`, `nU`)
"""
dir = self.s_cfg.comms.dir
cfg = self.s_cfg.comms.action
# Write actions into npy file
np.save(dir + cfg.f_name, actions)
os.mkdir(dir + cfg.rdy_name) # RDY signal
print('Actions saved')
# --------------------------------------------------------------------------
# SAMPLE ACTIONS
# --------------------------------------------------------------------------
def computeAction(self, states):
"""Computes the control actions given the observed system states.
Arguments:
states (np.array): Observed states, shape (`n_parts`, `nS`)
Returns:
np.array: Computed actions, with shape (`n_parts`, `nU`)
"""
self.pret_state_traj[:,
self.t, :] = states[:self.n_parts_pretrained, :]
self.lear_state_traj[:,
self.t, :] = states[self.n_parts_pretrained:, :]
# At least one part learned, not trained first step
if self.n_parts_learned > 0 and self.t != 0 and (
self.t) % self.train_freq == 0:
print("Training model...")
obs_in = self.lear_state_traj[:, self.t -
self.train_freq:self.t, :].reshape(
-1,
self.lear_state_traj.shape[-1])
obs_out = self.lear_state_traj[:, self.t - self.train_freq +
1:self.t + 1, :].reshape(
-1,
self.lear_state_traj.shape[-1])
acs = self.lear_action_traj[:, self.t -
self.train_freq:self.t, :].reshape(
-1,
self.lear_action_traj.shape[-1])
self.policyLear.train(obs_in, obs_out, acs)
# COMPUTE ACTION
action = np.zeros((self.s_cfg.env.n_parts, 2))
lastTempId = None
for part in range(self.s_cfg.env.n_parts):
print("Sampling actions %d/%d" % (part, self.s_cfg.env.n_parts))
if part < self.n_parts_pretrained: # Pretrained policy
action[part, :], self.pred_cost_pret[
self.t, part] = self.policyPret.act(states[part, :],
self.t,
get_pred_cost=True)
else: # Learned policy
# Change target
if self.c_ler_cfg.ctrl_cfg.change_target:
for i in range(len(
self.c_ler_cfg.ctrl_cfg.n_parts_targets)):
if (part - self.n_parts_pretrained
) < self.c_ler_cfg.ctrl_cfg.n_parts_targets[i]:
if not i == lastTempId:
lastTempId = i
self.policyLear.changeTargetCost(
self.c_ler_cfg.ctrl_cfg.targets[i])
break
if self.t < self.train_freq: # Do not predict cost
action[part, :] = self.policyLear.act(states[part, :],
self.t,
get_pred_cost=False)
self.pred_cost_lear[self.t,
part - self.n_parts_pretrained] = 0
else:
action[part, :], self.pred_cost_lear[self.t,part-self.n_parts_pretrained] = \
self.policyLear.act(states[part, :], self.t, get_pred_cost=True)
# Force inputs from q/v , q/sqrt(v)
if (self.c_ler_cfg.ctrl_cfg.force.on and # setting enabled
(part >= self.c_ler_cfg.ctrl_cfg.force.start_part - 1)
and # suitable part
self.t >= self.c_ler_cfg.ctrl_cfg.force.init_buffer -
1): # past initial buffer time
part_rel = part - self.c_ler_cfg.ctrl_cfg.force.start_part + 1 # part w.r.t. first forced part
t_rel = self.t - self.c_ler_cfg.ctrl_cfg.force.init_buffer + 1 # t w.r.t. first event t
n_after_event = t_rel % self.c_ler_cfg.ctrl_cfg.force.delta # t w.r.t. last event t
print(
"t %d, t_rel %d, n_after_event %d, part %d, part_rel %d"
% (self.t, t_rel, n_after_event, part, part_rel))
part_repeat = int(
part_rel /
(self.c_ler_cfg.ctrl_cfg.force.n_parts *
len(self.c_ler_cfg.ctrl_cfg.force.n_repeats))
) # disregard if its upper/lower bound
part_repeat_2 = part_rel % (
self.c_ler_cfg.ctrl_cfg.force.n_parts *
len(self.c_ler_cfg.ctrl_cfg.force.n_repeats))
repeat_i = int(part_repeat_2 /
self.c_ler_cfg.ctrl_cfg.force.n_parts
) # determines how many repeats
print(
"part_repeat %d, part_repeat2 %d, repeat_i %d, n_repeats %d"
% (part_repeat, part_repeat_2, repeat_i,
self.c_ler_cfg.ctrl_cfg.force.n_repeats[repeat_i]))
if n_after_event < self.c_ler_cfg.ctrl_cfg.force.n_repeats[
repeat_i]: # Update frequency
lev = int(t_rel / self.c_ler_cfg.ctrl_cfg.force.delta
) # Update number
v = self.c_ler_cfg.ctrl_cfg.force.fixed_speed
if part_repeat == 0: # Upper bound
upper = self.c_ler_cfg.ctrl_cfg.force.upper_init + self.c_ler_cfg.ctrl_cfg.force.upper_delta * lev
q = upper * np.sqrt(v)
print(
"For part %d, power forced %d (upper limit %d)"
% (part, q, upper))
else: # Lower bound
lower = self.c_ler_cfg.ctrl_cfg.force.lower_init + self.c_ler_cfg.ctrl_cfg.force.lower_delta * lev
q = lower * v
print(
"For part %d, power forced %d (lower limit %d)"
% (part, q, lower))
action[part, :] = [v, q]
self.pret_action_traj[:,
self.t, :] = action[:self.n_parts_pretrained, :]
self.lear_action_traj[:,
self.t, :] = action[self.n_parts_pretrained:, :]
self.t += 1
return action
def initAction(self):
""" Returns the initial action vector.
This function is required because an initial layer must be built before
any feedback is available.
Returns:
np.array: Initial action vector with shape (`n_parts`, `nU`)
"""
return np.ones(
(self.s_cfg.env.n_parts, 2)) * self.s_cfg.env.init_params
def log(self):
""" Logs the state and action trajectories, as well as the predicted cost,
which may be of interest to tune some algorithmic parameters.
"""
for i in range(len(self.save_dirs)):
np.save(self.save_dirs[i] + "pret_state_traj.npy",
self.pret_state_traj)
np.save(self.save_dirs[i] + "pret_action_traj.npy",
self.pret_action_traj)
np.save(self.save_dirs[i] + "pret_pred_cost.npy",
self.pred_cost_pret)
np.save(self.save_dirs[i] + "lear_state_traj.npy",
self.lear_state_traj)
np.save(self.save_dirs[i] + "lear_action_traj.npy",
self.lear_action_traj)
np.save(self.save_dirs[i] + "lear_pred_cost.npy",
self.pred_cost_lear)
def loop(self):
""" While within the time horizon, read the states provided by the `Machine`
class, and compute and save the corresponding actions.
Allows the class functionality to be conveniently used as follows::
cluster = Cluster(s_cfg, cp_cfg, cl_cfg)
cluster.loop()
"""
self.sendAction(self.initAction())
while self.t < self.H:
states = self.getStates()
actions = self.computeAction(states)
self.sendAction(actions)
self.log()
class Cluster:
def __init__(self, shared_cfg, pretrained_cfg, learned_cfg):
self.s_cfg = shared_cfg
self.c_pre_cfg = pretrained_cfg
self.c_ler_cfg = learned_cfg
self.policyPret = MPC(pretrained_cfg.ctrl_cfg)
self.policyLear = MPC(learned_cfg.ctrl_cfg)
self.t = 0
self.H = shared_cfg.env.horizon
self.train_freq = learned_cfg.train_freq
self.n_parts = shared_cfg.env.n_parts
self.n_parts_pretrained = pretrained_cfg.n_parts
self.n_parts_learned = learned_cfg.n_parts
assert self.n_parts_pretrained+self.n_parts_learned == self.n_parts
self.pret_state_traj = np.zeros((self.n_parts_pretrained, self.H, shared_cfg.env.nS))
self.pret_action_traj = np.zeros((self.n_parts_pretrained, self.H, 2))
self.lear_state_traj = np.zeros((self.n_parts_learned, self.H, shared_cfg.env.nS))
self.lear_action_traj = np.zeros((self.n_parts_learned, self.H, 2))
self.pred_cost_pret = np.zeros((self.H, self.n_parts_pretrained))
self.pred_cost_lear = np.zeros((self.H, self.n_parts_learned))
self.save_dirs = [shared_cfg.save_dir1, shared_cfg.save_dir2]
# --------------------------------------------------------------------------
# SAMPLE ACTIONS
# --------------------------------------------------------------------------
def computeAction(self, states):
"""Return control action given the current machine state"""
self.pret_state_traj[:, self.t, :] = states[:self.n_parts_pretrained, :]
self.lear_state_traj[:, self.t, :] = states[self.n_parts_pretrained:, :]
# At least one part learned, not trained first step
if self.n_parts_learned > 0 and self.t!=0 and (self.t)%self.train_freq==0:
print("Training model...")
obs_in = self.lear_state_traj[:, self.t-self.train_freq:self.t, :].reshape(-1, self.lear_state_traj.shape[-1])
obs_out = self.lear_state_traj[:, self.t-self.train_freq+1:self.t+1, :].reshape(-1, self.lear_state_traj.shape[-1])
acs = self.lear_action_traj[:, self.t-self.train_freq:self.t, :].reshape(-1, self.lear_action_traj.shape[-1])
self.policyLear.train(obs_in, obs_out, acs)
action = np.zeros((self.s_cfg.env.n_parts, 2))
for part in range(self.s_cfg.env.n_parts):
print("Sampling actions %d/%d" % (part, self.s_cfg.env.n_parts))
if part < self.n_parts_pretrained: # Pretrained policy
action[part, :], self.pred_cost_pret[self.t,part] = self.policyPret.act(states[part, :], self.t, get_pred_cost=True)
else: # Learned policy
if self.t < self.train_freq: # Do not predict cost
action[part, :] = self.policyLear.act(states[part, :], self.t, get_pred_cost=False)
self.pred_cost_lear[self.t,part-self.n_parts_pretrained] = 0
else:
action[part, :], self.pred_cost_lear[self.t,part-self.n_parts_pretrained] = \
self.policyLear.act(states[part, :], self.t, get_pred_cost=True)
self.pret_action_traj[:, self.t, :] = action[:self.n_parts_pretrained, :]
self.lear_action_traj[:, self.t, :] = action[self.n_parts_pretrained:, :]
self.t+=1
return action
def initAction(self):
# Init with 1.125, 110
print("Initial action is 1.125, 110")
return np.ones((self.s_cfg.env.n_parts, 2)) * [1.125, 110]
def loop(self):
self.sendAction(self.initAction())
while self.t < self.H:
states = self.getStates()
actions = self.computeAction(states)
self.sendAction(actions)
self.log()
def log(self):
np.save("tttpret_state_traj.npy", self.pret_state_traj)
np.save("tttpret_action_traj.npy", self.pret_action_traj)
np.save("tttpret_pred_cost.npy", self.pred_cost_pret)
np.save("tttlear_state_traj.npy", self.lear_state_traj)
np.save("tttlear_action_traj.npy", self.lear_action_traj)
np.save("tttlear_pred_cost.npy", self.pred_cost_lear)