def get_history(demand_type,TS,lt, agent_pos,AI_choice):
Mu = 10
Sigma = 5
periods = 40
'''
Je ne vois pas comment est-ce qu'on peut éviter de faire le if comme tu avais proposé Thierry.
Dans un moment ou l'autre il va falloir lire la valeur dans demand_type et créer l'instance de la bonne classe
Donc soit on doit faire ce lecture ici dans la création des classes soit dans une fonction dans le fichier des Demandes
De toute le façon on aura un if avec tous les cas et ensuite la création de la demande avec les bon paramêtres
'''
if demand_type == "Seasonal":
demand = Demand.Seasonal_Demand(15, 5, 0, 1.5, 0, Mu - 2, Sigma)
elif demand_type == "Growing":
demand = Demand.Growing_Demand(0,(2*Mu/periods), 0, Sigma)
elif demand_type == "Sporadic":
demand = Demand.Sporadic_Demand (Mu,0.2,5)
elif demand_type == "Gaussian":
demand= Demand.Gaussian_Demand(Mu, Sigma, min_value = 0, max_value = 100)
elif demand_type =="Uniform":
demand = Demand.Uniform_Demand(Mu,Mu+1,Step = 1)
elif demand_type == "Growingseasonal":
demand = Demand.Growing_Seasonal_Demand(1,[Mu*0.5,Mu* 0.8,Mu*0.7,Mu*0.9,Mu,Mu,Mu*0.9,Mu*1.2,Mu,Mu*1.1,Mu*1.5,Mu*2], Sigma)
elif demand_type == "Mixedseasonal":
demand = Demand.Mixed_Saisonnalities_Demand(Mu, [1,2,2,2,3,4,4,2,1,1,4,3,2,3,4,2,1,2,3,2,3,2,3,2,3],[0.6,0.8,0.7,0.9], Sigma)
elif demand_type == "Growthstable":
demand = Demand.Growth_Stable_Demand(0, 1, Mu + 5, Sigma)
game_params = {
'client_demand': demand,
'lead_times':[ld.Constant_LeadTime(lt), ld.Constant_LeadTime(lt),
ld.Constant_LeadTime(lt), ld.Constant_LeadTime(lt)],
'AI_possible_actions': np.arange(-10,11),
'm' :1,
'shortage_cost':get_optimal_gaussian_SC(TS, Mu = Mu, Sigma= Sigma, lead_time=lt),
'holding_cost':1,
'initial_inventory': Mu*lt,
'number_periods':periods,
'use_backorders':1,
'state_features':["IL" ,"d", "BO", "RS", "OO","t"],
'AI_DN':[10,10], # Not implemented yet
'TS':TS
}
list_agents = ['BS20','BS20', 'BS20' , 'BS20']
list_agents[agent_pos-1] = 'BNC'
test_agents = generate_agents(list_agents, game_params)
bg = BG.BeerGame()
env = Env.Environment(test_agents, game_params)
env.reset()
bg.play(env, test_agents, train = False, display = False , display_all=False)
# History
#print("History keys : ", env.history.keys())
#print(env.history['IL'])
# IMPORTANT NOTE!!
# There are 4 agent
# They're indices are 1, 2 ,3 ,4
#print("Agent's 1 stock history : ", env.history['IL'][:,1])
#print("Agent's 2 stock history : ", env.history['IL'][:,2])
#print("Agent's 3 stock history : ", env.history['IL'][:,3])
#print("Agent's 4 stock history : ", env.history['IL'][:,4])
return env.history
def __init__(self, index, vision,freq, mu, sigma, label = "BS", lead_time = ld.Uniform_LeadTime(2,2,1), BS_level = 20):
Agent.__init__(self, index = index, lead_time = lead_time, label = label+"("+str(BS_level)+")")
self.BS_level = BS_level
self.vision = vision
self.freq = freq
self.lastupdate = 0
self.average = mu
def __init__(self, index = 0, lead_time = None, label="agent"):
self.i = index
self.label = label
self.is_AI_agent = False
if lead_time:
self.lead_time = lead_time
else:
self.lead_time = ld.Uniform_LeadTime(2,2,1)
def __init__(self, index, sigma, TS, mu, label = "BS_Gauss", lead_time = ld.Constant_LeadTime(1)):
"""
Args:
index : position agent dans la chaine
sigma : sigma de la demande gaussienne à lequel l'agent va faire face
TS : souhaité par le client
mu : mu de la demande gaussienne à lequel l'agent va faire face
label :
lead_time :
"""
global dict_z
z = dict_z[str(TS)]
self.BS_level = int(np.sqrt(lead_time.Mean) * sigma * z + (lead_time.Mean * mu)) + 1
#Base stock = point de commande de stock (lead_time * mu) + stock de securité
Agent.__init__(self, index = index, lead_time = lead_time, label = label+"("+str(self.BS_level)+")")
def __init__(self, index, sigma, TS, mu, label = "SS_Gauss", lead_time = ld.Uniform_LeadTime(2,2,1)):
"""
index : position agent dans la chaine
sigma : sigma de la demande gaussienne à lequel l'agent va faire face
TS : souhaité par le client
mu : mu de la demande gaussienne à lequel l'agent va faire face
label :
lead_time :
"""
Agent.__init__(self, index = index, lead_time = lead_time, label = label)
self.mu = mu
self.leadtime=lead_time
global dict_z
z = dict_z[str(TS)]
#Base stock = point de commande de stock (lead_time * mu) + stock de securité
self.BS_level = int(np.sqrt(self.leadtime.Mean) * sigma * z + (self.leadtime.Mean) * self.mu)+1
def __init__ (self, index, sigma, TS, demand , label = "SS_Seas", lead_time = ld.Uniform_LeadTime(2,2,1), BS_level = 20):
"""
Args:
index : agent's index
sigma : standard deviation
TS : Taux de service
demand : end client seasonal demand instance
nb_periodes : number of periods that will have the demand. Used to create the list of averages per period
label : agent's label
lead_time : lead time of the agent
"""
self.BS_level_list = []
demand_avg = demand.demand_avg
self.size = len(demand_avg)
global dict_z
z = dict_z[str(TS)]
for t in range(self.size):
self.BS_level_list.append(int(np.sqrt(lead_time.Mean) * sigma * z + demand_avg[t]*lead_time.Mean))
import Demand as Demand
import LeadTime as ld
import numpy as np
DEFAULT_PARAMS = {
'client_demand': Demand.Uniform_Demand(1,10,Step = 1),
'lead_times':[ld.Uniform_LeadTime(2,3), ld.Uniform_LeadTime(2,3),
ld.Uniform_LeadTime(2,3), ld.Uniform_LeadTime(2,3)],
'AI_possible_actions': np.arange(-10,11),
'm' :1,
'shortage_cost':4,
'holding_cost':1,
'initial_inventory':20,
'number_periods':52,
'use_backorders':1,
'state_features':["IL" ,"d", "BO", "RS", "OO"],
'AI_DN':[10,10]
}
def __init__(self, index, label = "BS", lead_time = ld.Uniform_LeadTime(2,2,1), BS_level = 20):
Agent.__init__(self, index = index, lead_time = lead_time, label = label+"("+str(BS_level)+")")
self.BS_level = BS_level
def __init__(self, index, lead_time = ld.Uniform_LeadTime(2,2,1), RND_possible_actions = [-5, 0, 5], label = "RND"):
Agent.__init__(self, index = index, lead_time = lead_time, label = label)
self.RND_possible_actions = RND_possible_actions
def Use_Dicts(global_variables,demand_variables):
for i in range(len(global_variables)):
for j in range(len(demand_variables)):
reload_all()
#%matplotlib qt5
# RE DEFINE PATH TO THE RESULTS FOLDER
path = 'C:/Users/danie/Dropbox/BeerGame/'
TS = global_variables[i]['TS']
Mu = global_variables[i]['mu']
Sigma = global_variables[i]['sigma']
constant_ld = global_variables[i]['ltavg']
periods = 40
'''
old way of using all demand types
#il faut choisir le type de demand et les actions possibles
#ensuite lancer avec les variations des autres variables avec le dictionnaire crée
'''
demand_type = global_variables[i]['demand_type']
if demand_type == "Seasonal":
demand = Demand.Seasonal_Demand(15, 5, 0, 1.5, 0, Mu - 2, Sigma)
elif demand_type == "Growing":
demand = Demand.Growing_Demand(0,(2*Mu/periods), 0, Sigma)
elif demand_type == "Sporadic":
demand = Demand.Sporadic_Demand(Mu,0.2,5)
#demand.generate(periods)
#bench_agent = Agent.BS_Agent_Gauss(1, Sigma, TS, Mu)
elif demand_type == "Gaussian":
demand= Demand.Gaussian_Demand(Mu, Sigma, min_value = 0, max_value = 100)
#demand = Demand.Gaussian_Demand(global_variables[i]['Mu'],global_variables[i]['Sigma'],global_variables[i]['Min'],global_variables[i]['Max'])
elif demand_type =="Uniform":
demand = Demand.Uniform_Demand(Mu ,Mu,Step = 1)
elif demand_type == "Growingseasonal":
demand = Demand.Growing_Seasonal_Demand(1,[Mu*0.5,Mu* 0.8,Mu*0.7,Mu*0.9,Mu,Mu,Mu * 0.9,Mu*1.2,Mu,Mu*1.1,Mu*1.5,Mu*2], Sigma)
elif demand_type == "Mixedseasonal":
demand = Demand.Mixed_Saisonnalities_Demand(Mu, [1,1,2,2,2,3,4,4,2,1,1,4],[0.6,0.8,0.7,0.9], Sigma)
elif demand_type == "Growthstable":
demand = Demand.Growth_Stable_Demand(0, 1, Mu + 5, Sigma)
else:
print("Did not recognize demand type")
break
bench_agent = demand.bench_agent(global_variables[i]['pos'],global_variables[i]['TS'],periods)
game_params = {
'client_demand':demand,
'lead_times':[ld.Constant_LeadTime(global_variables[i]['lt'][0]), ld.Constant_LeadTime(global_variables[i]['lt'][1]),
ld.Constant_LeadTime(global_variables[i]['lt'][2]), ld.Constant_LeadTime(global_variables[i]['lt'][3])],
'AI_possible_actions': np.arange(-10,10),
'm' : global_variables[i]['m'],
'shortage_cost':get_optimal_gaussian_SC(TS, Mu = Mu, Sigma= Sigma, lead_time=constant_ld),
'TS' : TS,
'holding_cost':1,
'initial_inventory':constant_ld * Mu + 2* Sigma,
'number_periods':periods,
'use_backorders':0,
'state_features':["IL" ,"d", "BO", "RS", "OO","t"],
'AI_DN':[10,10], # Not implemented yet
'comparison_agent' : bench_agent
}
'''
Alternatives to above to be more flexible
{
'client_demand': demand,
'lead_times':(global_variables[i]['leadtimes'], global_variables[i]['leadtimes'],
global_variables[i]['leadtimes'], global_variables[i]['leadtimes']),
'initial_inventory':global_variables[i]['leadtimes'].Mean*10,
}
Need to make changes to functions that creates the dictionary list
'''
list_agents = ['BS20','BS20', 'BS20' , 'BS20']
list_agents[global_variables[i]['pos']] = 'DQN'
agents = generate_agents(list_agents, game_params)
trainer = Tr.Trainer(agents, game_params)
comparator = trainer.generate_comparator(min_BS_level = 5, max_BS_level = 20)
trainer.train2(400)
AI_Agent = trainer.best_AI_agent #.get_AI_agent()
AI_Agent.label = 'best DQN'
comparator.update_AI_Agents([trainer.best_AI_agent, trainer.get_AI_agent()])
comparator.launch_comparison()
comparator.histograms()
comparator.one_game_results([trainer.get_AI_agent()])
importlib.reload(Saver)
saver = Saver.Saver(path)
saver.clean_results_folder()
saver.save(trainer)