我对下面提供的代码有一些问题。我正在开发python 3.6。我已经重新安装了Python和运行代码所需的所有模块。一般来说,我所做的一切都是基于这个tutorial。
问题描述:
当我运行这段代码时,我得到了以下警告,并且根本没有输出。我不明白这些警告是什么意思,也不知道我如何解决它。如果有任何帮助,我将不胜感激。
导入警告(来自warnings模块):
"D:\Users\Rafal\AppData\Local\Programs\Python\Python36\lib\site packages\h5py__init__.py",第36行._conv导入register_converters as _register_converters FutureWarning:不推荐将issubdtype的第二个参数从float转换为np.floating。将来,它将被视为np.float64 == np.dtype(float).type
和:
[33mWARN: gym.spaces.Box自动检测到的数据类型为。请提供显式数据类型。[0M
由我运行的代码:
import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter
LR = 1e-3
env = gym.make("CartPole-v0")
env.reset()
goal_steps = 500
score_requirement = 50
initial_games = 10000
def initial_population():
# [OBS, MOVES]
training_data = []
# all scores:
scores = []
# just the scores that met our threshold:
accepted_scores = []
# iterate through however many games we want:
for _ in range(initial_games):
score = 0
# moves specifically from this environment:
game_memory = []
# previous observation that we saw
prev_observation = []
# for each frame in 200
for _ in range(goal_steps):
# choose random action (0 or 1)
action = random.randrange(0,2)
# do it!
observation, reward, done, info = env.step(action)
# notice that the observation is returned FROM the action
# so we'll store the previous observation here, pairing
# the prev observation to the action we'll take.
if len(prev_observation) > 0 :
game_memory.append([prev_observation, action])
prev_observation = observation
score+=reward
if done: break
# IF our score is higher than our threshold, we'd like to save
# every move we made
# NOTE the reinforcement methodology here.
# all we're doing is reinforcing the score, we're not trying
# to influence the machine in any way as to HOW that score is
# reached.
if score >= score_requirement:
accepted_scores.append(score)
for data in game_memory:
# convert to one-hot (this is the output layer for our neural network)
if data[1] == 1:
output = [0,1]
elif data[1] == 0:
output = [1,0]
# saving our training data
training_data.append([data[0], output])
# reset env to play again
env.reset()
# save overall scores
scores.append(score)
# just in case you wanted to reference later
training_data_save = np.array(training_data)
np.save('saved.npy',training_data_save)
# some stats here, to further illustrate the neural network magic!
print('Average accepted score:',mean(accepted_scores))
print('Median score for accepted scores:',median(accepted_scores))
print(Counter(accepted_scores))
return training_data
https://stackoverflow.com/questions/49218443
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