我对下面提供的代码有些问题。
我正在使用Python 3.6进行工作。
我已经重新安装了Python和运行代码所需的所有模块。
总的来说，我基于这个tutorial做了所有的事情。
问题描述:
当我运行这段代码时，我得到以下警告，但没有任何输出。 我不明白这些警告意味着什么以及如何修复它们。 我将非常感激任何帮助。
警告（来自警告模块）：文件 "D:\Users\Rafal\AppData\Local\Programs\Python\Python36\lib\site packages\h5py__init__.py", line 36 from ._conv import register_converters as _register_converters FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type
还有：
[33mWARN: gym.spaces.Box autodetected dtype as . Please provide explicit dtype.[0m
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