From 4bf746ee668539397cc1779ebb65e1b6238980b5 Mon Sep 17 00:00:00 2001
From: Thomas Simonini <simonini.thomas.pro@gmail.com>
Date: Fri, 24 Feb 2023 14:06:06 +0100
Subject: [PATCH] Update video

---
 units/en/unitbonus3/language-models.mdx | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/units/en/unitbonus3/language-models.mdx b/units/en/unitbonus3/language-models.mdx
index 9d873c3..db36cf7 100644
--- a/units/en/unitbonus3/language-models.mdx
+++ b/units/en/unitbonus3/language-models.mdx
@@ -20,9 +20,7 @@ There is therefore a potential synergy between LMs which can bring knowledge abo
 
 As a first attempt, the paper [“Grounding Large Language Models with Online Reinforcement Learning”](https://arxiv.org/abs/2302.02662v1) tackled the problem of **adapting or aligning a LM to a textual environment using PPO**. They showed that the knowledge encoded in the LM lead to a fast adaptation to the environment (opening avenue for sample efficiency RL agents) but also that such knowledge allowed the LM to better generalize to new tasks once aligned.
 
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-</video>
+<video src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit12/papier_v4.mp4" type="video/mp4" />
 
 Another direction studied in [“Guiding Pretraining in Reinforcement Learning with Large Language Models”](https://arxiv.org/abs/2302.06692) was to keep the LM frozen but leverage its knowledge to **guide an RL agent’s exploration**. Such method allows the RL agent to be guided towards human-meaningful and plausibly useful behaviors without requiring a human in the loop during training.