def load_top_individuals(population_size, network_width, network_hidden_layers,
observation_space, action_space, environment_name,
total_population_counter):
initial_population = []
for i in range(NUM_SELECTED_FOR_REPRODUCTION):
action_predictor_model = create_model(network_width,
network_hidden_layers,
observation_space, action_space)
dir_path = os.path.realpath(".")
fn = dir_path + "/" + "" + versionName + "" + str(i) + "-weights.h5"
print("filepath ", fn)
if os.path.isfile(fn):
print("loading weights")
action_predictor_model.load_weights("" + versionName + "" +
str(i) + "-weights.h5")
else:
print("File ", "" + versionName + "" + str(i) + "-weights.h5",
" does not exis. Retraining... ")
indiv = Individual(generationID=0,
indivID=total_population_counter,
network=action_predictor_model)
total_population_counter += 1
initial_population.append(indiv)
return initial_population, total_population_counter
def initialize_population(population_size,network_width,network_hidden_layers, observation_space, action_space, environment_name,total_population_counter):
initial_population = []
for i in range (population_size):
action_predictor_model = create_model(network_width,network_hidden_layers, observation_space, action_space)
indiv = Individual(generationID=0, indivID=total_population_counter , network = action_predictor_model)
total_population_counter += 1
initial_population.append(indiv)
return initial_population, total_population_counter
def populate_next_generation(generationID, top_individuals, population_size,
network_width, network_hidden_layers,
observation_space, action_space,
total_population_counter):
newPop = top_individuals
num_selected = len(top_individuals)
for i in range(population_size - len(top_individuals)):
newModel = create_model(network_width, network_hidden_layers,
observation_space, action_space)
model1 = top_individuals[0].network
model2 = top_individuals[1].network
sz = len(newModel.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = newModel.layers[k].get_weights()
w1 = model1.layers[k].get_weights()
w2 = model2.layers[k].get_weights()
if np.alen(w) > 0:
#print("k==>",k)
#w[0][0] = combine_weights(w[0][0],w1[0][0],w2[0][0])
for j in range(np.alen(w[0])):
y = w[0][j]
y1 = w1[0][j]
y2 = w2[0][j]
for l in range(np.alen(y)):
z = y[l]
#chrose randomly from top_individuals
parentID = randint(0, num_selected - 1)
parent = top_individuals[parentID]
parentNetwork = parent.network
z1 = parentNetwork.layers[k].get_weights()[0][j][l]
z = z1 + 0.0
y[l] = z
w[0][j] = y
newModel.layers[k].set_weights(w)
top_individuals.append(
Individual(generationID, total_population_counter, newModel))
total_population_counter += 1
return top_individuals, total_population_counter
def populate_next_generation(generationID, top_individuals, population_size,
network_width, network_hidden_layers,
observation_space, action_space,
total_population_counter):
newPop = top_individuals
for i in range(population_size - len(top_individuals)):
newModel = create_model(network_width, network_hidden_layers,
observation_space, action_space)
model1 = top_individuals[0].network
model2 = top_individuals[1].network
sz = len(newModel.layers)
#if largeNoise:
# print("Setting Large Noise!")
for k in range(sz):
w = newModel.layers[k].get_weights()
w1 = model1.layers[k].get_weights()
w2 = model2.layers[k].get_weights()
if np.alen(w) > 0:
#print("k==>",k)
#w[0][0] = combine_weights(w[0][0],w1[0][0],w2[0][0])
for j in range(np.alen(w[0])):
y = w[0][j]
y1 = w1[0][j]
y2 = w2[0][j]
for l in range(np.alen(y)):
z = y[l]
z1 = y1[l]
z2 = y2[l]
if np.random.rand(1) > 0.5:
z = z1 + 0.0
else:
z = z2 + 0.0
y[l] = z
w[0][j] = y
newModel.layers[k].set_weights(w)
top_individuals.append(
Individual(generationID, total_population_counter, newModel))
total_population_counter += 1
return top_individuals, total_population_counter