current_week = math.ceil(current_time / (7*24*60)) #Calculating the current week
for key, value in sim.due_wafers.items():
rolling_window.append(value[current_week:current_week+max_length_of_window]) #Adding only the values from current week up till the window length
buffer_list = [] # This list stores value of previous unfinished wafers count
buffer_list.append(sum(value[:current_week]))
rolling_window.extend([buffer_list])
c = sum(rolling_window, [])
state_rep.extend(c) # Appending the rolling window to state space
return state_rep
# Create the factory simulation object
my_sim = fact_sim.FactorySim(sim_time, machine_dict, recipes, lead_dict, wafers_per_box, part_mix, n_part_mix)
# start the simulation
my_sim.start()
# Retrieve machine object for first action choice
mach = my_sim.next_machine
# Save the state and allowed actions at the start for later use in training examples
state = get_state(my_sim)
allowed_actions = my_sim.allowed_actions
# The action space is a list of tuples of the form [('ht1',0), ('ht1',1), ..., ('ht2', 0), ...] indicating the head
# types and sequence steps for all allowed actions.
action_space = list(chain.from_iterable(my_sim.station_HT_seq.values()))
action_size = len(action_space)
state_size = len(state)
# Creating the DQN agent
dqn_agent = DeepQNet.DQN(state_space_dim= state_size, action_space= action_space)
] # This list stores value of previous unfinished wafers count
buffer_list.append(sum(value[:current_week]))
rolling_window.extend([buffer_list])
c = sum(rolling_window, [])
state_rep.extend(c) # Appending the rolling window to state space
# print(len(state_rep))
return state_rep
# Create the factory simulation object
my_sim = fact_sim.FactorySim(sim_time,
machine_dict,
recipes,
lead_dict,
wafers_per_box,
part_mix,
n_part_mix,
break_mean=break_mean,
repair_mean=repair_mean)
# start the simulation
my_sim.start()
# Retrieve machine object for first action choice
mach = my_sim.next_machine
# Save the state and allowed actions at the start for later use in training examples
state = get_state(my_sim)
allowed_actions = my_sim.allowed_actions
# The action space is a list of tuples of the form [('ht1',0), ('ht1',1), ..., ('ht2', 0), ...] indicating the head
# types and sequence steps for all allowed actions.
action_space = list(chain.from_iterable(my_sim.station_HT_seq.values()))
action_size = len(action_space)
for key, value in sim.due_wafers.items():
rolling_window.append(
value[current_week:current_week + max_length_of_window]
) #Adding only the values from current week up till the window length
buffer_list = [
] # This list stores value of previous unfinished wafers count
buffer_list.append(sum(value[:current_week]))
rolling_window.extend([buffer_list])
c = sum(rolling_window, [])
state_rep.extend(c) # Appending the rolling window to state space
return state_rep
# Create the factory simulation object
my_sim = fact_sim.FactorySim(sim_time, machine_dict, recipes, lead_dict,
wafers_per_box, wip_levels)
# start the simulation
my_sim.start()
# Retrieve machine object for first action choice
mach = my_sim.next_machine
# Save the state and allowed actions at the start for later use in training examples
state = get_state(my_sim)
allowed_actions = my_sim.allowed_actions
# The action space is a list of tuples of the form [('ht1',0), ('ht1',1), ..., ('ht2', 0), ...] indicating the head
# types and sequence steps for all allowed actions.
action_space = list(chain.from_iterable(my_sim.station_HT_seq.values()))
action_size = len(action_space)
state_size = len(state)
# Creating the A2C agent
a2c_agent = ActorCritic.A2CAgent(state_size=state_size,
