doc(nyz): add ch7 recompute docs

opendilab · Jul 21, 2023 · ad37ad3 · ad37ad3
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-<html><head><meta charset="utf-8"></meta><title>Annonated Algorithm Visualization</title><link rel="stylesheet" href="pylit.css?v=1"></link><link rel="stylesheet" href="solarized.css"></link><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" integrity="sha384-Juol1FqnotbkyZUT5Z7gUPjQ9gzlwCENvUZTpQBAPxtusdwFLRy382PSDx5UUJ4/" crossorigin="anonymous"></link><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js" integrity="sha384-97gW6UIJxnlKemYavrqDHSX3SiygeOwIZhwyOKRfSaf0JWKRVj9hLASHgFTzT+0O" crossorigin="anonymous"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js" integrity="sha384-+VBxd3r6XgURycqtZ117nYw44OOcIax56Z4dCRWbxyPt0Koah1uHoK0o4+/RRE05" crossorigin="anonymous" onload="renderMathInElement(document.body);" defer="True"></script><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/lib/codemirror.min.css"></link><script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/codemirror.min.js"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/mode/python/python.min.js"></script></head><body><div class="section" id="section0"><div class="docs doc-strings"><p><a href="index.html"><b>HOME</b></a></p><a href="https://github.com/opendilab/PPOxFamily" target="_blank"><img alt="GitHub" style="max-width:100%;" src="https://img.shields.io/github/stars/opendilab/PPOxFamily?style=social"></img></a>  <a href="https://space.bilibili.com/1112854351?spm_id_from=333.337.0.0" target="_blank"><img alt="bilibili" style="max-width:100%;" src="https://img.shields.io/badge/bilibili-video%20course-blue"></img></a>  <a href="https://twitter.com/OpenDILab" rel="nofollow" target="_blank"><img alt="twitter" style="max-width:100%;" src="https://img.shields.io/twitter/follow/opendilab?style=social"></img></a><br><a href="https://github.com/opendilab/PPOxFamily" target="_blank">View code on GitHub</a></div></div><div class="section" id="section1"><div class="docs doc-strings"><h1><a href="https://github.com/opendilab/PPOxFamily">PPO × Family PyTorch 注解文档</a></h1><img alt="logo" src="./imgs/ppof_logo.png"></img><p>作为 PPO × Family 决策智能入门公开课的“算法-代码”注解文档，力求发掘 PPO 算法的每一个细节，帮助读者快速掌握设计决策人工智能的万能钥匙。</p></div></div><div class="section" id="section1"><div class="docs doc-strings"><h2>各章节代码解读示例目录</h2><h4>开启决策 AI 探索之旅</h4><li><a href="./pg_zh.html">策略梯度（PG）算法核心代码</a>  |  <a href="./pg.html">Policy Gradient core loss function</a></li><li><a href="./a2c_zh.html">A2C 算法核心代码</a>  |  <a href="./a2c.html">A2C core loss function</a></li><li><a href="./ppo_zh.html">PPO 算法核心代码</a>  |  <a href="./ppo.html">PPO core loss function</a></li><br><h4>解构复杂动作空间</h4><li><a href="./discrete_zh.html">PPO 建模离散动作空间</a>  |  <a href="./discrete.html">PPO in discrete action space</a></li><li><a href="./continuous_zh.html">PPO 建模连续动作空间</a>  |  <a href="./continuous.html">PPO in continuous action space</a></li><li><a href="./hybrid_zh.html">PPO 建模混合动作空间</a>  |  <a href="./hybrid.html">PPO in hybrid action space</a></li><br><h4>表征多模态观察空间</h4><li><a href="./encoding_zh.html">特征编码的各种技巧</a>  |  <a href="./encoding.html">Encoding methods for vector obs space</a></li><li><a href="./mario_wrapper_zh.html">图片动作空间的各类环境包装器</a>  |  <a href="./mario_wrapper.html">Env wrappers for image obs space</a></li><li><a href="./gradient_zh.html">神经网络梯度计算的代码解析</a>  |  <a href="./gradient.html">Automatic gradient mechanism</a></li><br><h4>统筹多智能体</h4><li><a href="./marl_network.html">Multi-Agent cooperation network</a></li><li><a href="./independentpg.html">Independent policy gradient training</a></li><li><a href="./mapg.html">Multi-Agent policy gradient training</a></li><li><a href="./mappo.html">Multi-Agent PPO training</a></li><br><h4>挖掘黑科技</h4><li><a href="./gae.html">GAE technique used in PPO</a></li><li><a href="./grad_clip_norm_zh.html">PPO 中使用的梯度范数裁剪</a>  |  <a href="./grad_clip_norm.html">Gradient norm clip trick used in PPO</a></li><li><a href="./grad_clip_value.html">Gradient value clip trick used in PPO</a></li><li><a href="./grad_ignore.html">Gradient ignore trick used in PPO</a></li><li><a href="./orthogonal_init.html">Orthogonal initialization of networks used in PPO</a></li><li><a href="./dual_clip.html">Dual clip trick used in PPO</a></li><li><a href="./value_clip.html">Value clip trick used in PPO</a></li></div></div><div class="section" id="section-final"><div class="docs doc-strings"><p><i>如果读者关于本文档有任何问题和建议，可以在 GitHub 提 issue 或是直接发邮件给我们 ([email protected]) 。</i></p></div></div></body><script type="text/javascript">
+<html><head><meta charset="utf-8"></meta><title>Annonated Algorithm Visualization</title><link rel="stylesheet" href="pylit.css?v=1"></link><link rel="stylesheet" href="solarized.css"></link><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" integrity="sha384-Juol1FqnotbkyZUT5Z7gUPjQ9gzlwCENvUZTpQBAPxtusdwFLRy382PSDx5UUJ4/" crossorigin="anonymous"></link><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js" integrity="sha384-97gW6UIJxnlKemYavrqDHSX3SiygeOwIZhwyOKRfSaf0JWKRVj9hLASHgFTzT+0O" crossorigin="anonymous"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js" integrity="sha384-+VBxd3r6XgURycqtZ117nYw44OOcIax56Z4dCRWbxyPt0Koah1uHoK0o4+/RRE05" crossorigin="anonymous" onload="renderMathInElement(document.body);" defer="True"></script><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/lib/codemirror.min.css"></link><script src="https://cdn.jsdelivr.net/npm/[email protected]/lib/codemirror.min.js"></script><script src="https://cdn.jsdelivr.net/npm/[email protected]/mode/python/python.min.js"></script></head><body><div class="section" id="section0"><div class="docs doc-strings"><p><a href="index.html"><b>HOME</b></a></p><a href="https://github.com/opendilab/PPOxFamily" target="_blank"><img alt="GitHub" style="max-width:100%;" src="https://img.shields.io/github/stars/opendilab/PPOxFamily?style=social"></img></a>  <a href="https://space.bilibili.com/1112854351?spm_id_from=333.337.0.0" target="_blank"><img alt="bilibili" style="max-width:100%;" src="https://img.shields.io/badge/bilibili-video%20course-blue"></img></a>  <a href="https://twitter.com/OpenDILab" rel="nofollow" target="_blank"><img alt="twitter" style="max-width:100%;" src="https://img.shields.io/twitter/follow/opendilab?style=social"></img></a><br><a href="https://github.com/opendilab/PPOxFamily" target="_blank">View code on GitHub</a></div></div><div class="section" id="section1"><div class="docs doc-strings"><h1><a href="https://github.com/opendilab/PPOxFamily">PPO × Family PyTorch 注解文档</a></h1><img alt="logo" src="./imgs/ppof_logo.png"></img><p>作为 PPO × Family 决策智能入门公开课的“算法-代码”注解文档，力求发掘 PPO 算法的每一个细节，帮助读者快速掌握设计决策人工智能的万能钥匙。</p></div></div><div class="section" id="section1"><div class="docs doc-strings"><h2>各章节代码解读示例目录</h2><h4>开启决策 AI 探索之旅</h4><li><a href="./pg_zh.html">策略梯度（PG）算法核心代码</a>  |  <a href="./pg.html">Policy Gradient core loss function</a></li><li><a href="./a2c_zh.html">A2C 算法核心代码</a>  |  <a href="./a2c.html">A2C core loss function</a></li><li><a href="./ppo_zh.html">PPO 算法核心代码</a>  |  <a href="./ppo.html">PPO core loss function</a></li><br><h4>解构复杂动作空间</h4><li><a href="./discrete_zh.html">PPO 建模离散动作空间</a>  |  <a href="./discrete.html">PPO in discrete action space</a></li><li><a href="./continuous_zh.html">PPO 建模连续动作空间</a>  |  <a href="./continuous.html">PPO in continuous action space</a></li><li><a href="./hybrid_zh.html">PPO 建模混合动作空间</a>  |  <a href="./hybrid.html">PPO in hybrid action space</a></li><br><h4>表征多模态观察空间</h4><li><a href="./encoding_zh.html">特征编码的各种技巧</a>  |  <a href="./encoding.html">Encoding methods for vector obs space</a></li><li><a href="./mario_wrapper_zh.html">图片动作空间的各类环境包装器</a>  |  <a href="./mario_wrapper.html">Env wrappers for image obs space</a></li><li><a href="./gradient_zh.html">神经网络梯度计算的代码解析</a>  |  <a href="./gradient.html">Automatic gradient mechanism</a></li><br><h4>统筹多智能体</h4><li><a href="./marl_network.html">Multi-Agent cooperation network</a></li><li><a href="./independentpg.html">Independent policy gradient training</a></li><li><a href="./mapg.html">Multi-Agent policy gradient training</a></li><li><a href="./mappo.html">Multi-Agent PPO training</a></li><br><h4>挖掘黑科技</h4><li><a href="./gae.html">GAE technique used in PPO</a></li><li><a href="./recompute.html">Recompute adv trick used in PPO</a></li><li><a href="./grad_clip_norm_zh.html">PPO 中使用的梯度范数裁剪</a>  |  <a href="./grad_clip_norm.html">Gradient norm clip trick used in PPO</a></li><li><a href="./grad_clip_value.html">Gradient value clip trick used in PPO</a></li><li><a href="./grad_ignore.html">Gradient ignore trick used in PPO</a></li><li><a href="./orthogonal_init.html">Orthogonal initialization of networks used in PPO</a></li><li><a href="./dual_clip.html">Dual clip trick used in PPO</a></li><li><a href="./value_clip.html">Value clip trick used in PPO</a></li></div></div><div class="section" id="section-final"><div class="docs doc-strings"><p><i>如果读者关于本文档有任何问题和建议，可以在 GitHub 提 issue 或是直接发邮件给我们 ([email protected]) 。</i></p></div></div></body><script type="text/javascript">
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