深度学习在游戏和金融领域的应用

### 深度学习在游戏与金融领域的应用 #### 深度学习在游戏中的应用 在游戏领域，深度学习有着广泛的应用，尤其是在开发能够玩游戏的人工智能方面。以玩Atari游戏为例，我们可以使用深度Q学习（Deep Q-Learning）方法来构建一个智能体。 ##### 策略选择与epsilon值调整 在网络的测试阶段，epsilon值会显著降低，从而更倾向于采用开发策略。以下是在Python中实现该策略的代码： ```python def select(self): ## Select a Q Value from the Base Q Network QValue = self.QValue.eval(feed_dict = {self.iputVal:[self.currentState]})[0] ## Initialize actions as zeros action = np.zeros(self.action) action_index = 0 ## If this timestep is the first, start with a random action if self.timeStep % 1 == 0: ## if random.random() <= self.starting_ep: a_index = random.randrange(self.action) action[a_index] = 1 else: action_index = np.argmax(QValue) action[action_index] = 1 else: action[0] = 1 ## Anneal the value of epsilon if self.starting_ep > self.ending_ep and self.timeStep > self.observe: self.starting_ep -= (self.starting_ep - self.ending_ep) / self.explore ``` ##### 训练方法 我们定义了一个训练方法`trainingPipeline`，它接收动作输入和目标Q值输入，并使用均方误差（MSE）损失函数来计算损失。同时，使用RMSProp优化器进行优化。 ```python def trainingPipeline(self): self.actionInput = tf.placeholder("float",[None,self.actions]) self.yInput = tf.placeholder("float", [None]) Q_Action = tf.reduce_sum(tf.multiply(self.QValue, self.actionInput), reduction_indices = 1) self.cost = tf.reduce_mean(tf.square(self.yInput - Q_Action)) self.trainStep = tf.train.RMSPropOptimizer(0.00025,0.99,0.0,1e-6).minimize(self.cost) ``` ##### 网络训练 训练函数`train`会从经验回放内存中随机抽取小批量数据进行训练，并计算每个批次的Q值。同时，会在特定迭代次数保存网络权重和状态。 ```python def train(self): ''' Training procedure for the Q Network''' minibatch = random.sample(self.replayBuffer, 32) stateBatch = [data[0] for data in minibatch] actionBatch = [data[1] for data in minibatch] rewardBatch = [data[2] for data in minibatch] nextBatch = [data[3] for data in minibatch] batch = [] qBatch = self.QValueT.eval(feed_dict = {self.inputValT: nextBatch}) for i in range(0, 32): terminal = minibatch[i][4] if terminal: batch.append(rewardBatch[i]) else: batch.append(rewardBatch[i] + self.gamma * np.max(qBatch[i])) self.trainStep.run(feed_dict={ self.yInput : batch, self.actionInput : actionBatch, self.inputVal : stateBatch }) ## Save the network on specific iterations if self.timeStep % 10000 == 0: self.saver.save(self.session, './savedweights' + '-atari', global_step = self.timeStep) ``` ##### 经验回放 `er_replay`函数用于将新的经验添加到经验回放内存中，并在满足条件时进行训练。 ```python def er_replay(self, nextObservation, action, reward, terminal): newState = np.append(nextObservation, self.currentState[:,:,1:], axis = 2) self.replayMemory.append((self.currentState, action, reward, newState, terminal)) if len(self.replayBuffer) > 40000: self.replayBuffer.popleft() if self.timeStep > self.explore: self.trainQNetwork() self.currentState = newState self.timeStep += 1 ``` ##### 运行网络 以下是运行网络的代码，包括初始化环境、预处理输入数据、选择动作等步骤。 ```python import cv2 import sys from deepQ import deepQ import numpy as np import gym class Atari: def __init__(self): self.env = gym.make('SpaceInvaders-v0') self.env.reset() self.actions = self.env.action_space.n self.deepQ = deepQ(self.actions) self.action0 = 0 def preprocess(self,observation): observation = cv2.cvtColor(cv2.resize(observation, (84, 110)), cv2.COLOR_BGR2GRAY) observation = observation[26:110,:] ret, observation = cv2.threshold(ob ```