最近，我进入了神经网络。我目前正在尝试DQN。我已经能够让他们使用许多Atari教程，但发现我无法理解整个问题，所以我为DQN编写了一个简单的模式来处理，即A大于B，如果不是，则给出1给出0。然后根据是否正确对神经网络给予1或0的分数。不幸的是，我无法了解到这个简单的问题。有人可以帮我这个忙吗？
# -*- coding: utf-8 -*-
import random
import numpy as np
import env
import gym
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
EPISODES = 1000
class DQNAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=2000)
#self.gamma = 0.95 # discount rate
self.gamma = 0 # discount rate
self.epsilon = 0.5 # exploration rate
self.epsilon_min = 0.01
self.epsilon_decay = 0.985
self.learning_rate = 0.001
self.model = self._build_model()
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Dense(2, input_dim=self.state_size, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(self.action_size, activation='relu'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
action = np.argmax(self.model.predict(state)[0])
return action
def replay(self, batch_size):
minibatch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in minibatch:
print("stating")
target = reward
if not done:
target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
target_f = self.model.predict(state)
target_f[0][action] = target
print("Reward: " + str(reward))
print("Target: " + str(target))
print(action)
print(self.gamma*np.amax(self.model.predict(next_state)[0]))
print(state)
print(target_f)
self.model.fit(state,target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
if __name__ == "__main__":
state_size = 2
action_size = 2
#timeRange = 1440
timeRange = 998
agent = DQNAgent(state_size, action_size)
# agent.load("./save/cartpole-master.h5")
done = False
batch_size = 500
totalScore=0
for e in range(EPISODES):
env_state = env.GameState()
env_state.reset()
state = env_state.step(0)[1]
#state = np.reshape(state, [1, state_size])
totalreward = 0
for time in range(timeRange):
#if time==timeRange-1:
#done = True
action = agent.act(state)
reward, next_state = env_state.step(action)
totalreward += reward
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
print("episode: {}/{}, score: {}, e: {:.2}"
.format(e, EPISODES, env_state.money+env_state.shares*env_state.sharePrice, agent.epsilon))
totalScore+=env_state.money+env_state.shares*env_state.sharePrice
done = False
break
if len(agent.memory) > batch_size:
print("replaying")
print("Total reward: "+str(totalreward))
totalreward = 0
#print(agent.memory)
agent.replay(batch_size)
# if e % 10 == 0:
# agent.save("./save/cartpole.h5")
最佳答案
这来自您在模型中使用的激活：
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Dense(2, input_dim=self.state_size, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(self.action_size, activation='relu'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
使用RELU激活无法学习>或
但是，当您更改激活方式时：
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Dense(2, input_dim=self.state_size, activation='sigmoid'))
model.add(Dense(2, activation='sigmoid'))
model.add(Dense(2, activation='sigmoid'))
model.add(Dense(self.action_size, activation='sigmoid'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return model
现在，出现了其他种类的非线性，这些非线性使网络可以学习那些显然“简单”的比较任务。
我希望这有帮助 ：-）