def eval_genomes(genomes, config):
for genome_id, genome in genomes:
genome.fitness = 0
net = helper.create_net(genome, config)
total_reward = 0
observation = env.reset()
max_pos = 0
max_speed = 0
print(genome_id)
for _ in range(200):
# if genome_id > 200:
# env.render()
actions = net.activate(observation)
# eng_lr = helper.scale(0,1,-1,1, eng_lr)
observation, reward, done, info = env.step(actions)
total_reward += reward
max_pos = max(max_pos, abs(observation[0]))
max_speed = max(max_speed, abs(observation[1]))
if done:
break
# Avg fitness over n runs
genome.fitness = total_reward + max_pos + max_speed
print(genome.fitness)
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = helper.create_net(genome, config)
# 1600 steps to "solve" as per environment requests (most are expected to fail anyway)
# 24 inputs, 14 features + 10 lidar
# 4 outputs, -1 to 1 for each joint
total_reward = 0
observation = env.reset()
stopped_time = 0
for _ in range(800):
env.render()
action = net.activate(observation)
observation, reward, done, info = env.step(action)
if abs(observation[2]) < 0.001:
stopped_time += 1
if stopped_time >= 50:
reward = -100
done = True
total_reward += reward
if done:
break
genome.fitness = total_reward
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
genome.fitness = 0
net = helper.create_net(genome, config)
total_reward = 0
observation = env.reset()
for _ in range(1000):
env.render()
actions = net.activate(observation)
# eng_lr = helper.scale(0,1,-1,1, eng_lr)
a, _ = max(enumerate(actions), key=lambda i_s: i_s[1])
observation, reward, done, info = env.step(a)
total_reward += reward
if done:
break
# Avg fitness over n runs
genome.fitness = total_reward
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = helper.create_net(genome, config)
sse = 0
for x, y in zip(x_test, y_test):
y_pred = net.activate(x)
sse += sum([(p - c)**2 for p, c in zip(y_pred, y)])
genome.fitness = -sse
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = helper.create_net(genome, config)
total_reward = 0
observation = env.reset()
for _ in range(200):
cos_th, sin_th, th_dot = observation
th_dot = helper.scale(-8, 8, -1, 1, th_dot)
action = (net.activate([cos_th, sin_th, th_dot])[0])
observation, reward, done, info = env.step([action])
total_reward += reward
if done:
break
genome.fitness = total_reward
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = helper.create_net(genome, config)
total_correct = 0
# eval on the i'th bucket
for x, y in zip(x_test, y_test):
y_pred = net.activate(x)
y_pred_i = max(range(len(y_pred)), key=lambda j: y_pred[j])
y_test_i = max(range(len(y)), key=lambda j: y[j])
# Get a measure of "how" correct it is
correct = 1 - (
(y[y_test_i] -
y_pred[y_pred_i]))**2 if y_pred_i == y_test_i else 0
avg_diff = sum([(y_p - y_t)**2
for y_p, y_t in zip(y_pred, y)]) / len(y_pred)
total_correct += correct - avg_diff
genome.fitness = total_correct
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = helper.create_net(genome, config)
# 1600 steps to "solve" as per environment requests (most are expected to fail anyway)
# 24 inputs, 14 features + 10 lidar
# 4 outputs, -1 to 1 for each joint
total_reward = 0
observation = env.reset()
for _ in range(800):
env.render()
action = net.activate(observation)
# These are all standard sigmoid outputs = [0,1]
# Adjust to [-1,1]
# for i,a in enumerate(action):
# action[i] = helper.scale(0,1,-1,1, a)
observation, reward, done, info = env.step(action)
total_reward += reward
if done:
break
genome.fitness = total_reward
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
genome.fitness = 0
net = helper.create_net(genome, config)
total_reward = 0
observation = env.reset()
max_pos = 0
max_speed = 0
min_speed = 0
for _ in range(200):
position_scaled = helper.scale(-1.2, 0.6, 0, 1, observation[0])
velocity_scaled = helper.scale(-0.07, 0.07, -1, 1, observation[1])
# if genome_id > 200:
# env.render()
# print(position_scaled, velocity_scaled)
actions = net.activate([position_scaled, velocity_scaled])
action, _ = max(enumerate(actions), key=lambda i_s: i_s[1])
# eng_lr = helper.scale(0,1,-1,1, eng_lr)
observation, reward, done, info = env.step(action)
total_reward += reward
max_pos = max(max_pos, observation[0])
max_speed = max(max_speed, abs(observation[1]))
min_speed = min(min_speed, abs(observation[1]))
if done:
break
# Avg fitness over n runs
genome.fitness = total_reward + max_pos + max_speed + (max_speed -
min_speed)
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
genome.fitness = 0
net = helper.create_net(genome, config)
total_reward = 0
observation = env.reset()
for _ in range(1000):
# env.render()
eng_m, eng_lr = net.activate(observation)
# eng_lr = helper.scale(0,1,-1,1, eng_lr)
observation,reward,done,info = env.step([eng_m, eng_lr])
total_reward += reward
if done:
break
# Avg fitness over n runs
genome.fitness = total_reward
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
total_reward = 0
net = helper.create_net(genome, config)
