我是Python新手，我在代码中遇到了一个问题。我尝试为深度Q网络程序构建自定义环境。我的环境名称是"FooEnv"。但是当我运行主代码时，我在FooEnv.reset((行中遇到了此错误
类型对象 'FooEnv' 没有属性 'reset'
这是我的主要代码，我在这里称之为"FooEnv"：
import json
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import sgd
from FooEnv import FooEnv
class ExperienceReplay(object):
def __init__(self, max_memory=100, discount=.9):
self.max_memory = max_memory
self.memory = list()
self.discount = discount
def remember(self, states, game_over):
# memory[i] = [[state_t, action_t, reward_t, state_t+1], game_over?]
self.memory.append([states, game_over])
if len(self.memory) > self.max_memory:
del self.memory[0]
def get_batch(self, model, batch_size=10):
len_memory = len(self.memory)
num_actions = model.output_shape[-1]
# env_dim = self.memory[0][0][0].shape[1]
env_dim = self.memory[0][0][0].shape[1]
inputs = np.zeros((min(len_memory, batch_size), env_dim))
targets = np.zeros((inputs.shape[0], num_actions))
for i, idx in enumerate(np.random.randint(0, len_memory,
size=inputs.shape[0])):
state_t, action_t, reward_t, state_tp1 = self.memory[idx][0]
game_over = self.memory[idx][1]
inputs[i:i+1] = state_t
# There should be no target values for actions not taken.
# Thou shalt not correct actions not taken #deep
targets[i] = model.predict(state_t)[0]
Q_sa = np.max(model.predict(state_tp1)[0])
if game_over: # if game_over is True
targets[i, action_t] = reward_t
else:
# reward_t + gamma * max_a' Q(s', a')
targets[i, action_t] = reward_t + self.discount * Q_sa
return inputs, targets
if __name__ == "__main__":
# parameters
epsilon = .1
num_actions = 2
epoch = 1000
max_memory = 500
hidden_size = 100
batch_size = 50
input_size = 2
f_c=[2.4*10**9]
eta_Los=[1]
eta_NLos=[2]
x_threshold = [5]
model = Sequential()
model.add(Dense(hidden_size, input_shape=(2, ), activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=.2), "mse")
# Define environment/game
env = FooEnv(f_c, eta_Los, eta_NLos)
# Initialize experience replay object
exp_replay = ExperienceReplay(max_memory=max_memory)
FooEnv.reset()
这是我的FooEnv代码：
import numpy as np
import math
class FooEnv(object):
def __init__(self, f_c, eta_Los, eta_NLos):
self.f_c = f_c
self.eta_Los = eta_Los
self.eta_NLos = eta_NLos
self.num_actions = 2
def reset(self):
state=self.state
E_Consumtion, Average_Delay_UAV, Average_DeLay_FAP = state
E_Consumtion=0
Average_Delay_UAV=0
Average_DeLay_FAP=0
self.state = np.append(E_Consumtion,Average_Delay_UAV,Average_DeLay_FAP)
self.steps_beyond_done = None
return np.array(self.state)
如果您能帮助我解决这个问题，我将不胜感激。
FooEnv 是一个类，env 是该类的对象。 您要重置对象，而不是类。