From 263e490b762e9241caa7e7deaaae5c162db0c372 Mon Sep 17 00:00:00 2001
From: Trinkle23897 <463003665@qq.com>
Date: Tue, 16 Jun 2020 16:54:16 +0800
Subject: [PATCH] fix #79

---
 README.md                     | 2 +-
 docs/_static/css/style.css    | 8 ++++++++
 docs/tutorials/cheatsheet.rst | 2 +-
 3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 61b3bcc..150489a 100644
--- a/README.md
+++ b/README.md
@@ -299,4 +299,4 @@ If you find Tianshou useful, please cite it in your publications.
 
 Tianshou was previously a reinforcement learning platform based on TensorFlow. You can check out the branch [`priv`](https://github.com/thu-ml/tianshou/tree/priv) for more detail. Many thanks to [Haosheng Zou](https://github.com/HaoshengZou)'s pioneering work for Tianshou before version 0.1.1.
 
-We would like to thank [TSAIL](http://ml.cs.tsinghua.edu.cn/) and [Institute for Artificial Intelligence, Tsinghua University](http://ai.tsinghua.edu.cn/) for providing such an excellent AI research platform.
+We would like to thank [TSAIL](http://ml.cs.tsinghua.edu.cn/) and [Institute for Artificial Intelligence, Tsinghua University](http://ml.cs.tsinghua.edu.cn/thuai/) for providing such an excellent AI research platform.
diff --git a/docs/_static/css/style.css b/docs/_static/css/style.css
index 6da8faf..b9f323f 100644
--- a/docs/_static/css/style.css
+++ b/docs/_static/css/style.css
@@ -116,6 +116,14 @@ footer p {
     display: none;
 }
 
+.ethical-fixedfooter {
+    display: none;
+}
+
+.ethical-content {
+    display: none;
+}
+
 /* For hidden headers that appear in TOC tree */
 /* see http://stackoverflow.com/a/32363545/3343043 */
 .rst-content .hidden-section {
diff --git a/docs/tutorials/cheatsheet.rst b/docs/tutorials/cheatsheet.rst
index 878dd59..d193ae3 100644
--- a/docs/tutorials/cheatsheet.rst
+++ b/docs/tutorials/cheatsheet.rst
@@ -3,7 +3,7 @@ Cheat Sheet
 
 This page shows some code snippets of how to use Tianshou to develop new algorithms / apply algorithms to new scenarios.
 
-By the way, some of these issues can be resolved by using a ``gym.wrapper``. It could be a universal solution in the policy-environment interaction.
+By the way, some of these issues can be resolved by using a ``gym.wrapper``. It could be a universal solution in the policy-environment interaction. But you can also use the batch processor :ref:`preprocess_fn`.
 
 .. _network_api: