From e2bbc63001ff8fb8eb6795160cfd9bb03a2384b5 Mon Sep 17 00:00:00 2001
From: Varuna Jayasiri <vpjayasiri@gmail.com>
Date: Mon, 21 Jun 2021 18:35:22 +0530
Subject: [PATCH] links

---
 docs/index.html      | 5 +++++
 labml_nn/__init__.py | 6 ++++++
 readme.md            | 6 ++++++
 3 files changed, 17 insertions(+)

diff --git a/docs/index.html b/docs/index.html
index c2c9d4ff..9ba7ab1d 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -110,6 +110,11 @@ implementations.</p>
 <li><a href="gan/stylegan/index.html">StyleGAN 2</a></li>
 </ul>
 <h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
+<h4>✨ <a href="cfr/index.html">Counterfactual Regret Minimization (CFR)</a></h4>
+<p>Solving games with incomplete information such as poker with CFR.</p>
+<ul>
+<li><a href="cfr/kuhn/index.html">Kuhn Poker</a></li>
+</ul>
 <h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4>
 <ul>
 <li><a href="rl/ppo/index.html">Proximal Policy Optimization</a> with
diff --git a/labml_nn/__init__.py b/labml_nn/__init__.py
index 14efd224..bf8302fc 100644
--- a/labml_nn/__init__.py
+++ b/labml_nn/__init__.py
@@ -50,6 +50,12 @@ implementations.
 
 #### ✨ [Sketch RNN](sketch_rnn/index.html)
 
+#### ✨ [Counterfactual Regret Minimization (CFR)](cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](cfr/kuhn/index.html)
+
 #### ✨ [Reinforcement Learning](rl/index.html)
 * [Proximal Policy Optimization](rl/ppo/index.html) with
  [Generalized Advantage Estimation](rl/ppo/gae.html)
diff --git a/readme.md b/readme.md
index 3250eecf..3b436ae4 100644
--- a/readme.md
+++ b/readme.md
@@ -56,6 +56,12 @@ implementations almost weekly.
 
 #### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
 
+#### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
+
 #### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
 * [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
  [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)