def __init__(self):
self.First_Gen = Genetic.Chromosomes_Offset()
self.Gen_S = Genetic.Gen_Selection()
self.Gen_C = Genetic.CrossOver()
self.Gen_M = Genetic.Mutation()
self.PathG = './CrS/'
self.PathS = './Score/score.txt'
self.PathE = './E_Cons/E_Cons.txt'
pass
def __init__(self):
self.First_Gen = Genetic.Chromosomes_Offset()
self.Gen_S = Genetic.Gen_Selection()
self.Gen_C = Genetic.CrossOver()
self.Gen_M = Genetic.Mutation()
self.Gen_List = self.Get_Gene()
self.score = self.Get_Score()
self.E_Cons = self.Get_ECons()
self.F_Cons = 10
def Mainthread():
for k in range(10):
if (k == 0):
Gen_C = Genetic.Chromosomes_Offset()
Gen_List = Gen_C.initGen(8)
for i in range(len(Gen_List)):
f = open('./CrS/' + str(i) + '.txt', 'w')
f.write(Gen_List[i][0])
f.write(Gen_List[i][1])
f.close()
Mgen = []
Sgen = []
for i in range(len(Gen_List)):
Mgen.append(Gen_List[i][0])
Sgen.append(Gen_List[i][1])
for i in range(len(Mgen)):
Model = Model_Converter.GeneticModel(Mgen[i], Sgen[i]).model
model_json = Model.to_json()
f = open('./model/model' + str(i) + '.json', 'w')
f.write(model_json)
f.close()
else:
Gen_M = gen_main.GenMain()
Gen_M.main()
for j in range(8):
json_file = open("./model/model" + str(i) + ".json", "r")
loaded_model_json = json_file.read()
json_file.close()
loaded_model = keras.models.model_from_json(loaded_model_json)
memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=loaded_model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=10,
target_model_update=1e-2,
policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=30000, visualize=False, verbose=2)
dqn.save_weights('t_score/dqn_' + str(k) + '_' + str(j) +
'{}_weights.h5f'.format(env_name),
overwrite=True)
Calc_E_Cons_and_Perfomance(dqn, j)
json_file = open("./model/model" + str(i) + ".json", "r")
loaded_model_json = json_file.read()
json_file.close()
loaded_model = tf.keras.models.model_from_json(loaded_model_json)
model.append(loaded_model)
return model
if __name__ == "__main__":
global_step = 0
# In case of BreakoutDeterministic-v3, always skip 4 frames
# Deterministic-v4 version use 4 actions
env = gym.make('BreakoutDeterministic-v4')
for j in range(10):
if (j == 0):
Gen_C = Genetic.Chromosomes_Offset()
Gen_List = Gen_C.initGen(8)
Mgen = []
Sgen = []
for i in range(len(Gen_List)):
Mgen.append(Gen_List[i][0])
Sgen.append(Gen_List[i][1])
for i in range(len(Mgen)):
Model = Model_Converter.GeneticModel(Mgen[i], Sgen[i]).model
model_json = Model.to_json()
with open("model/model" + str(i) + ".json", "w") as json_file:
json_file.write(model_json)
f = open('./Score/score.txt', 'w')
g = open('./E_Cons/E_Cons.txt', 'w')
f.close()
g.close()
