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# 用于存储章节的列表
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chapters = []
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# 遍历每一组文件
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# 每3000 个文件为一组
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for filename in group:
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# 打开并读取文件
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with open(os.path.join('./temp', filename), 'r') as f:
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data = json.load(f)
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# 为每个文件创建一个章节
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chapter = epub.EpubHtml(title=data['title'], file_name='chap_' + data['id'] + '.xhtml', lang='en')
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chapter.content = u'<h1>' + data['title'] + '</h1><p>' + data['text'] + '</p>'
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# 将章节添加到书中
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book.add_item(chapter)
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# 将章节添加到章节列表中
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chapters.append(chapter)
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# 定义书的结构
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book.toc = (epub.Link(chapters[0].file_name, 'Introduction', 'intro'),
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(epub.Section('Simple English Wikipedia'), chapters))
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# 将书的结构添加到书中
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book.add_item(epub.EpubNcx())
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book.add_item(epub.EpubNav())
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# 定义书的逻辑
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book.spine = ['nav'] + chapters
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# 写入文件
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# write to output folder
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# create if it doesn't exist
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if not os.path.exists('./output'):
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os.makedirs('./output')
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# epub.write_epub('simple' + str(i) + '.epub', book, {})
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epub.write_epub(os.path.join('./output', 'simple-english-wikiepedia-' + str(i) + '.epub'), book, {})
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print('完成创建电子书: simple-english-wikiepedia-' + str(i) + '.epub')
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# 打印大小
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print('电子书大小: {} KB'.format(os.path.getsize(os.path.join('./output', 'simple-english-wikiepedia-' + str(i) + '.epub')) / 1024))
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# <FILESEP>
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import copy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.optim.lr_scheduler import CosineAnnealingLR
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import wandb
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from IPython import embed
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from util import DEFAULT_DEVICE, compute_batched, update_exponential_moving_average
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EXP_ADV_MAX = 100.
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def asymmetric_l2_loss(u, tau):
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# from paper: "Offline Reinforcement Learning with Implicit Q-Learning" by Ilya et al.
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return torch.mean(torch.abs(tau - (u < 0).float()) * u**2)
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class IQL(nn.Module):
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def __init__(self, qf, vf, policy, max_steps,
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tau, alpha, value_lr=1e-4, policy_lr=1e-4, discount=0.99, beta=0.005):
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super().__init__()
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self.qf = qf.to(DEFAULT_DEVICE)
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self.q_target = copy.deepcopy(qf).requires_grad_(False).to(DEFAULT_DEVICE)
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self.vf = vf.to(DEFAULT_DEVICE)
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self.policy = policy.to(DEFAULT_DEVICE)
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self.v_optimizer = torch.optim.Adam(self.vf.parameters(), lr=value_lr)
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self.q_optimizer = torch.optim.Adam(self.qf.parameters(), lr=value_lr)
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self.policy_optimizer = torch.optim.Adam(self.policy.parameters(), lr=policy_lr)
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self.policy_lr_schedule = CosineAnnealingLR(self.policy_optimizer, max_steps)
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self.tau = tau
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self.alpha = alpha
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self.discount = discount
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self.beta = beta
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self.step = 0
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self.pretrain_step = 0
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def iql_update(self, observations, actions, next_observations, rewards, terminals):
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# the network will NOT update
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with torch.no_grad():
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target_q = self.q_target(observations, actions)
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next_v = self.vf(next_observations)
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v = self.vf(observations)
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adv = target_q - v
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v_loss = asymmetric_l2_loss(adv, self.tau)
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self.v_optimizer.zero_grad(set_to_none=True)
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v_loss.backward()
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self.v_optimizer.step()
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# Update Q function
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targets = rewards + (1. - terminals.float()) * self.discount * next_v
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qs = self.qf.both(observations, actions)
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