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16
docs/rl/ppo/index.html
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@ -3,24 +3,24 @@
<head> <head>
<meta http-equiv="content-type" content="text/html;charset=utf-8"/> <meta http-equiv="content-type" content="text/html;charset=utf-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0"/> <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
<meta name="description" content="An annotated implementation of Proximal Policy Optimization (PPO) algorithm in PyTorch."/> <meta name="description" content="An annotated implementation of Proximal Policy Optimization - PPO algorithm in PyTorch."/>
<meta name="twitter:card" content="summary"/> <meta name="twitter:card" content="summary"/>
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/> <meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta name="twitter:title" content="Proximal Policy Optimization (PPO)"/> <meta name="twitter:title" content="Proximal Policy Optimization - PPO"/>
<meta name="twitter:description" content="An annotated implementation of Proximal Policy Optimization (PPO) algorithm in PyTorch."/> <meta name="twitter:description" content="An annotated implementation of Proximal Policy Optimization - PPO algorithm in PyTorch."/>
<meta name="twitter:site" content="@labmlai"/> <meta name="twitter:site" content="@labmlai"/>
<meta name="twitter:creator" content="@labmlai"/> <meta name="twitter:creator" content="@labmlai"/>
<meta property="og:url" content="https://nn.labml.ai/rl/ppo/index.html"/> <meta property="og:url" content="https://nn.labml.ai/rl/ppo/index.html"/>
<meta property="og:title" content="Proximal Policy Optimization (PPO)"/> <meta property="og:title" content="Proximal Policy Optimization - PPO"/>
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/> <meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta property="og:site_name" content="LabML Neural Networks"/> <meta property="og:site_name" content="LabML Neural Networks"/>
<meta property="og:type" content="object"/> <meta property="og:type" content="object"/>
<meta property="og:title" content="Proximal Policy Optimization (PPO)"/> <meta property="og:title" content="Proximal Policy Optimization - PPO"/>
<meta property="og:description" content="An annotated implementation of Proximal Policy Optimization (PPO) algorithm in PyTorch."/> <meta property="og:description" content="An annotated implementation of Proximal Policy Optimization - PPO algorithm in PyTorch."/>
<title>Proximal Policy Optimization (PPO)</title> <title>Proximal Policy Optimization - PPO</title>
<link rel="shortcut icon" href="/icon.png"/> <link rel="shortcut icon" href="/icon.png"/>
<link rel="stylesheet" href="../../pylit.css"> <link rel="stylesheet" href="../../pylit.css">
<link rel="canonical" href="https://nn.labml.ai/rl/ppo/index.html"/> <link rel="canonical" href="https://nn.labml.ai/rl/ppo/index.html"/>
@ -72,7 +72,7 @@
<div class='section-link'> <div class='section-link'>
<a href='#section-0'>#</a> <a href='#section-0'>#</a>
</div> </div>
<h1>Proximal Policy Optimization (PPO)</h1> <h1>Proximal Policy Optimization - PPO</h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of
<a href="https://arxiv.org/abs/1707.06347">Proximal Policy Optimization - PPO</a>.</p> <a href="https://arxiv.org/abs/1707.06347">Proximal Policy Optimization - PPO</a>.</p>
<p>PPO is a policy gradient method for reinforcement learning. <p>PPO is a policy gradient method for reinforcement learning.

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docs/rl/ppo/readme.html
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@ -7,20 +7,20 @@
<meta name="twitter:card" content="summary"/> <meta name="twitter:card" content="summary"/>
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/> <meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta name="twitter:title" content="Proximal Policy Optimization (PPO)"/> <meta name="twitter:title" content="Proximal Policy Optimization - PPO"/>
<meta name="twitter:description" content=""/> <meta name="twitter:description" content=""/>
<meta name="twitter:site" content="@labmlai"/> <meta name="twitter:site" content="@labmlai"/>
<meta name="twitter:creator" content="@labmlai"/> <meta name="twitter:creator" content="@labmlai"/>
<meta property="og:url" content="https://nn.labml.ai/rl/ppo/readme.html"/> <meta property="og:url" content="https://nn.labml.ai/rl/ppo/readme.html"/>
<meta property="og:title" content="Proximal Policy Optimization (PPO)"/> <meta property="og:title" content="Proximal Policy Optimization - PPO"/>
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/> <meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta property="og:site_name" content="LabML Neural Networks"/> <meta property="og:site_name" content="LabML Neural Networks"/>
<meta property="og:type" content="object"/> <meta property="og:type" content="object"/>
<meta property="og:title" content="Proximal Policy Optimization (PPO)"/> <meta property="og:title" content="Proximal Policy Optimization - PPO"/>
<meta property="og:description" content=""/> <meta property="og:description" content=""/>
<title>Proximal Policy Optimization (PPO)</title> <title>Proximal Policy Optimization - PPO</title>
<link rel="shortcut icon" href="/icon.png"/> <link rel="shortcut icon" href="/icon.png"/>
<link rel="stylesheet" href="../../pylit.css"> <link rel="stylesheet" href="../../pylit.css">
<link rel="canonical" href="https://nn.labml.ai/rl/ppo/readme.html"/> <link rel="canonical" href="https://nn.labml.ai/rl/ppo/readme.html"/>
@ -72,7 +72,7 @@
<div class='section-link'> <div class='section-link'>
<a href='#section-0'>#</a> <a href='#section-0'>#</a>
</div> </div>
<h1><a href="https://nn.labml.ai/rl/ppo/index.html">Proximal Policy Optimization (PPO)</a></h1> <h1><a href="https://nn.labml.ai/rl/ppo/index.html">Proximal Policy Optimization - PPO</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of <p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of
<a href="https://arxiv.org/abs/1707.06347">Proximal Policy Optimization - PPO</a>.</p> <a href="https://arxiv.org/abs/1707.06347">Proximal Policy Optimization - PPO</a>.</p>
<p>PPO is a policy gradient method for reinforcement learning. <p>PPO is a policy gradient method for reinforcement learning.

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labml_nn/rl/ppo/__init__.py
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@ -1,11 +1,11 @@
""" """
--- ---
title: Proximal Policy Optimization (PPO) title: Proximal Policy Optimization - PPO
summary: > summary: >
An annotated implementation of Proximal Policy Optimization (PPO) algorithm in PyTorch. An annotated implementation of Proximal Policy Optimization - PPO algorithm in PyTorch.
--- ---
# Proximal Policy Optimization (PPO) # Proximal Policy Optimization - PPO
This is a [PyTorch](https://pytorch.org) implementation of This is a [PyTorch](https://pytorch.org) implementation of
[Proximal Policy Optimization - PPO](https://arxiv.org/abs/1707.06347). [Proximal Policy Optimization - PPO](https://arxiv.org/abs/1707.06347).

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labml_nn/rl/ppo/readme.md
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# [Proximal Policy Optimization (PPO)](https://nn.labml.ai/rl/ppo/index.html) # [Proximal Policy Optimization - PPO](https://nn.labml.ai/rl/ppo/index.html)
This is a [PyTorch](https://pytorch.org) implementation of This is a [PyTorch](https://pytorch.org) implementation of
[Proximal Policy Optimization - PPO](https://arxiv.org/abs/1707.06347). [Proximal Policy Optimization - PPO](https://arxiv.org/abs/1707.06347).

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setup.py
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@ -5,7 +5,7 @@ with open("readme.md", "r") as f:
setuptools.setup( setuptools.setup(
name='labml-nn', name='labml-nn',
version='0.4.91', version='0.4.93',
author="Varuna Jayasiri, Nipun Wijerathne", author="Varuna Jayasiri, Nipun Wijerathne",
author_email="vpjayasiri@gmail.com, hnipun@gmail.com", author_email="vpjayasiri@gmail.com, hnipun@gmail.com",
description="A collection of PyTorch implementations of neural network architectures and layers.", description="A collection of PyTorch implementations of neural network architectures and layers.",
@ -20,7 +20,7 @@ setuptools.setup(
'labml_helpers', 'labml_helpers.*', 'labml_helpers', 'labml_helpers.*',
'test', 'test',
'test.*')), 'test.*')),
install_requires=['labml>=0.4.103', install_requires=['labml>=0.4.109',
'labml-helpers>=0.4.76', 'labml-helpers>=0.4.76',
'torch', 'torch',
'einops', 'einops',