Datasets:
jblitzar
/
github-python

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