def run_change_machine_state(self):
while True:
yield simpy.AnyOf(self.env, [x.finished_control for x in self.task_list.control_tasks])
task = [tsk for tsk in self.task_list.control_tasks if tsk.finished_control.processed][0]
command = task.finished_control.value
if self.road_conditions.tor60.processed and int(task.finished_control.value[1]) == 4:
command = 'TOR60'
if int(task.finished_control.value[1]) <= self.road_conditions.get_state():
self.machine.on_event(self.env.timeout(0, value=command))
self.user_memory.update_machine_state(self.machine.get_state_index())
# task.update_awareness_param()
self.task_list.run_machine_triggered_tasks(command=command)
else:
print("WARNING: USER TAKE WRONG DECISION ON ",
str(int(self.env.now / 60)) + ':' + str(int(self.env.now % 60)).zfill(2))
previous_state = str(self.machine.state)
self.machine.on_event(self.env.timeout(0, value='Error'))
self.task_list.run_machine_triggered_tasks('Error')
yield self.env.timeout(2)
if previous_state == 'TOR10' or previous_state == 'TOR60':
self.machine.on_event(self.env.timeout(0, value='L0_switch'))
else:
self.machine.on_event(self.env.timeout(0, value=previous_state + '_switch'))
# print('switch_callback' + callback + ' ' + str(self.env.now))
task.finished_control = self.env.event()
def sim(self):
obs = self.get_obs()
while (not self.env.done):
yield self.simpy_env.timeout(
ACTION_WAIT_TIME) # Forces small gap between get_obs and act
action, agent_info = self.policy(obs)
self.action_event = self.simpy_env.process(
self.take_action(action))
try:
yield simpy.AnyOf(self.simpy_env,
[self.action_event, self.env.done_event])
except simpy.Interrupt:
pass
reward = self.get_reward()
self.observations.append(self.env.observation_space.flatten(obs))
self.actions.append(self.env.action_space.flatten(action))
self.rewards.append(reward)
self.agent_infos.append(agent_info)
self.env_infos.append({})
obs = self.get_obs()
self.offset_t_sojourn.append(
self.env.observation_space.flatten(obs)[-1])
def reset_and_sim(self, policies): self.simpy_env = simpy.Environment() self.done = False self.fire_extinguish_events = [simpy.Event(self.simpy_env) for i in range(self.n_fires)] fire_levels = [] for i, n in enumerate(self.num_fires_of_each_size): fire_levels += [i+1] * n if self.fire_locations is not None: self.fires = [ Fire(self, self.simpy_env, i, fire_levels[i], fl) for i, fl in enumerate(self.fire_locations) ] else: # we want to randomize fire_locations = ( 2.*np.random.random_sample((self.n_fires,2)) - 1.).tolist() self.fires = [ Fire(self, self.simpy_env, i, fire_levels[i], fl) for i, fl in enumerate(fire_locations) ] if self.start_positions is not None: self.env_agents = [ UAV(self, self.simpy_env, i, sp, sp, self.discount, policies[i]) for i,sp in enumerate(self.start_positions) ] else: # we want to randomize start_positions = ( 2.*np.random.random_sample((self.n_agents,2)) - 1.).tolist() self.env_agents = [ UAV(self, self.simpy_env, i, sp, sp, self.discount, policies[i]) for i,sp in enumerate(start_positions) ] # Process all UAVs for uav in self.env_agents: self.simpy_env.process( uav.sim() ) # Process all fires for fire in self.fires: self.simpy_env.process( fire.sim() ) done = simpy.AllOf(self.simpy_env, self.fire_extinguish_events) self.simpy_env.run(until = simpy.AnyOf(self.simpy_env, [done, self.simpy_env.timeout(MAX_SIMTIME)]) ) self.done = True if(True): for uav in self.env_agents: try: uav.action_event.interrupt() except RuntimeError: pass self.simpy_env.run(until = self.simpy_env.now*(1.0001)) assert sum([uav.get_reward() for uav in self.env_agents]) == 0, 'There were unaccounted for rewards' # Collect observations, actions, etc.. and return them observations = [ u.observations for u in self.env_agents] actions = [ u.actions for u in self.env_agents] rewards = [ u.rewards for u in self.env_agents] agent_infos = [ u.agent_infos for u in self.env_agents] env_infos = [ u.env_infos for u in self.env_agents] offset_t_sojourn = [ u.offset_t_sojourn for u in self.env_agents ] return observations, actions, rewards, agent_infos, env_infos, offset_t_sojourn
def step(self, actions):
# Takes an action set, outputs next observations, accumulated reward, done (boolean), info
# Convention is:
# If an agent is to act on this event, pass an observation and accumulated reward,
# otherwise, pass None
# "obs" variable will look like: [ [None], [None], [o3_t], [None], [o5_t] ]
# "rewards" will look like: [ None , None , r3_r , None , r5_t ]
# The action returned by the (decentralized) policy will look like
# [ None , None , a3_t , None , a5_t ]
for i, a in enumerate(actions):
if a is not None:
# need to bound action
action = max(min(a[0], MAX_STOP_TIME), MIN_STOP_TIME)
event = simpy.Event(self.simpy_env)
self.agent_event_list[i] = event
self.simpy_env.process(self.env_agents[i].change_traffic(
event, action))
self.simpy_env.run(
until=simpy.AnyOf(self.simpy_env, self.agent_event_list +
[self.max_simtime_event]))
whotriggered = who_triggered(self.agent_event_list)
# Get next_obs, rewards
# Check if max_simtime_reached
try:
self.max_simtime_event.ok
done = True
obs = [e.get_obs() for e in self.env_agents]
rewards = [e.get_reward() for e in self.env_agents]
except (AttributeError):
done = False
obs = [
self.env_agents[i].get_obs() if w else [None]
for i, w in enumerate(whotriggered)
]
rewards = [
self.env_agents[i].get_reward() if w else None
for i, w in enumerate(whotriggered)
]
return obs, rewards, done, {}
def worker_manager(self, env, batches):
try:
while True:
if options['to_stop']:
return
self.working = True
while True:
if options['to_stop']:
return
self.assigned_batch = scheduler(env, batches, self)
if numpy.size(self.assigned_batch) == 0:
batch_events = [
x.batch.process for x in batches
if x.batch.arrive_time > env.now
]
if numpy.size(batch_events) == 0:
return
print('--- WORKER ---'
) if options['verbose'] > 4 else None
print(
"worker", self.index, 'will resume at',
numpy.min([
x.batch.arrive_time for x in batches
if x.batch.arrive_time > env.now
])) if options['verbose'] > 4 else None
yield simpy.AnyOf(env, batch_events)
else:
break
print('--- WORKER ---') if options['verbose'] > 4 else None
print("batch", self.assigned_batch, "with deadline", self.assigned_batch.batch.deadline, "assigned") \
if options['verbose'] > 4 else None
self.assigned_batch.reserve_job()
yield env.timeout(
numpy.random.exponential(options['average_service_time']))
print('--- WORKER ---') if options['verbose'] > 4 else None
print('worker', self.index, 'finished a job at', env.now,
'for batch', self.assigned_batch.batch.index
) if options['verbose'] > 4 else None
self.assigned_batch.success_job(env)
self.assigned_batch = numpy.nan
except simpy.Interrupt as i:
print('--- WORKER ---') if options['verbose'] > 4 else None
print('job interrupted', env.now, 'because:',
i.cause) if options['verbose'] > 2 else None
if not self.assigned_batch == []:
self.assigned_batch.failure_job()
def process_queue(self):
retries = 0
while self.tx_queue:
frame = self.tx_queue[0]
# persistent process with exponential backoff
k = 1
while True:
wait_time = self.node.sim.random.randrange(k) * 5e-3
yield self.node.timeout(wait_time)
if self.node.phy.cca():
break
k = k * 2
self.node.phy.send_pdu(frame)
# wait for ack if this is a unicast frame
if frame.dst != BROADCAST_ADDR:
self.ack_event = self.node.create_event()
self.ack_event.wait_for = frame
duration = frame.nbits / self.node.phy.bitrate + 1e-3
yield simpy.AnyOf(self.node.sim.env, [
self.node.timeout(duration),
self.ack_event,
])
if self.ack_event.triggered:
retries = 0
self.tx_queue.popleft()
self.stat.total_tx_unicast += 1
else:
retries += 1
backoff_time = self.node.sim.random.randrange(
2**retries) * 5e-3
yield self.node.timeout(backoff_time)
self.stat.total_retransmit += 1
else:
retries = 0
self.tx_queue.popleft()
self.stat.total_tx_broadcast += 1
self.ack_event = None
def step(self, actions):
# Takes an action set, outputs next observations, accumulated reward, done (boolean), info
# Convention is:
# If an agent is to act on this event, pass an observation and accumulated reward,
# otherwise, pass None
# "obs" variable will look like: [ [None], [None], [o3_t], [None], [o5_t] ]
# "rewards" will look like: [ None , None , r3_r , None , r5_t ]
# The action returned by the (decentralized) policy will look like
# [ None , None , a3_t , None , a5_t ]
for i, a in enumerate(actions):
if a is not None:
if a >= 5:
# Agents wants to hold
self.env_agents[i].change_goal(hold_current=True)
else:
self.env_agents[i].change_goal(new_goal=a)
self.simpy_env.run(until=simpy.AnyOf(
self.simpy_env, self.uav_events + self.fire_events +
[self.max_simtime_event]))
agents_to_act = [False] * self.n_agents
# check if any fires triggered
fires_extinguished = who_triggered(self.fire_events)
for i in fires_extinguished:
agents_to_act = [True] * self.n_agents
self.fires[i].extinguish()
done = False
if (not any([f.status for f in self.fires])):
done = True
# check if any single agent triggered
uavs_to_act = who_triggered(self.uav_events)
for i in uavs_to_act:
agents_to_act[i] = True
# Get next_obs, rewards
try:
# Check if max_simtime_reached
self.max_simtime_event.ok
done = True
except (AttributeError):
pass
if (done):
obs = [e.get_obs() for e in self.env_agents]
rewards = [e.get_reward() for e in self.env_agents]
else:
obs = [
self.env_agents[i].get_obs() if w else [None]
for i, w in enumerate(agents_to_act)
]
rewards = [
self.env_agents[i].get_reward() if w else None
for i, w in enumerate(agents_to_act)
]
if (PRINTING): print('Obs: ', obs)
if (PRINTING): print('Reward: ', rewards)
return obs, rewards, done, {}
def passengerRun(self, env, passengerId):
entryFloorObj = floors_stat[self.entryFloor]
carList = [i for i in entryFloorObj.carsAtFloor]
def carAtList_generator():
return [
car.carAt_reactivate
for i, car in entryFloorObj.carsAtFloor.items()
]
def carArrivedList_generator():
return [
car.carArrived
for i, car in entryFloorObj.carsAtFloor.items()
]
yield simpy.AnyOf(env, carAtList_generator())
# wait and look if nobody were here before you:
yield env.timeout(0.0001)
while passengerId in entryFloorObj.queue:
for car_id in carList:
allocatedCar = cars_stat[car_id]
# car have to be at floor 0 and not overfilled:
if (allocatedCar.carUsage.count < allocatedCar.carCapacity
and entryFloorObj.carsAtFloor[car_id].carAt):
history.append(
'%6d passenger %d try to entry car %d Doors are %s'
% (env.now, passengerId, car_id,
allocatedCar.doorOpenedMonit.is_alive))
# passengers try entry car but in order of ascending id:
self.req = allocatedCar.carUsage.request(
priority=self.id)
# passenger wait for entry or for car doors closing:
yield self.req | allocatedCar.doorOpenedMonit
history.append(
'%6d passenger %d waited to entry car %d with priority %d'
' Doors are %s' %
(env.now, passengerId, car_id, self.id,
allocatedCar.doorOpenedMonit.is_alive))
# if not entry car:
if not self.req.triggered:
history.append(
'%6d passenger %d not leave queue and not entry car %d'
% (env.now, passengerId, car_id))
allocatedCar.carUsage.put_queue.remove(self.req)
else:
entryFloorObj.queue.pop(
entryFloorObj.queue.index(passengerId))
allocatedCar.passengersInCar.append(passengerId)
allocatedCar.passengersInCarDest.append(
self.destFloor)
passengers_stat[passengerId].depTime = env.now
history.append(
'%6d passenger %d leave queue and entry car %d LobbyQueue %s'
% (env.now, passengerId, car_id,
str(entryFloorObj.queue)))
yield env.process(
self.passengerUnloading(env, passengerId))
allocatedCar.carUsage.release(self.req)
self.runEnd = True
break
else:
pass
if not self.runEnd:
history.append('%6d passenger %d wait for any car' % (
env.now,
passengerId,
))
yield simpy.AnyOf(env, carArrivedList_generator())