def fitness_car_race(w, render: bool=False, steps=1000):
score = 0
nn.set_vectorized_weights(net, w, outs)
n = 2
for _ in range(n):
# env._max_episode_steps = steps
obs = env.reset()
last_obs = np.array(obs) / 255.0
# net.clear()
# fitness
s = 0
while True:
close = False
if render:
close = not env.render()
# print(obs)
obs = obs / 255.0
# if render:
# plt.cla()
# plt.imshow(obs[::8,::8,1])
# plt.pause(0.00001)
# determine action
res = net.predict(np.expand_dims(np.concatenate([
last_obs[::8,::8,1].flatten(),
obs[::8,::8,1].flatten()
]), 0))[0]
res = res * 2 - 1
action = res #np.argmax(res)
last_obs = obs
obs, reward, done, _ = env.step(action)
s += reward
if done or close:
break
score += s
if render:
print(s)
env.close()
if close:
break
return score / n
def fitness_xor(w: np.ndarray):
total, p = 0, 2
nn.set_vectorized_weights(net, w, outs)
out = net.predict(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]))
total += np.power(0 - out[0, 0], p)
total += np.power(1 - out[1, 0], p)
total += np.power(1 - out[2, 0], p)
total += np.power(0 - out[3, 0], p)
return 4 - total
def fitness_pong(w, render: bool = False, steps=1000):
score = 0
nn.set_vectorized_weights(net, w, outs)
for _ in range(1):
# env._max_episode_steps = steps
obs = env.reset()
# fitness
s = 0
while True:
close = False
if render:
close = not env.render()
# print(obs)
obs = obs / 256
# determine action
res = net.predict(np.expand_dims(obs, 0))[0]
action = np.argmax(res)
obs, reward, done, _ = env.step(action)
s += reward
if done or close:
break
score += s
if render:
print(s)
env.close()
if close:
break
return score
def fitness_cartpole(w: np.ndarray, render: bool = False, steps=1000):
score = 0
nn.set_vectorized_weights(net, w, outs)
n = 10
if render:
n = 1
for _ in range(n):
env._max_episode_steps = steps
obs = env.reset()
# total reward (fitness score)
s = 0
while True:
close = False
if render:
close = not env.render()
# print(obs)
# determine action to take
res = net.predict(np.expand_dims(obs, 0))[0]
action = np.argmax(res)
obs, reward, done, _ = env.step(action)
s += reward
if done or close:
break
score += s
if render:
print(s)
env.close()
return score / n
1.0,
500,
10,
min_sigma=1e-3,
big_sigma=1e1,
wait_iter=100)
try:
for i in range(1000):
scores = []
pop = e.ask()
# eval population
for ind in pop:
scores.append(fitness_xor(ind))
e.tell(scores)
max_score = np.max(scores)
# if max_score >= 3.9:
if i == 99:
ind = pop[np.argmax(scores)]
print(ind)
nn.set_vectorized_weights(net, ind, outs)
out = net.predict(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]))
print(out)
break
finally:
pass