diff --git a/units/en/unit8/introduction.mdx b/units/en/unit8/introduction.mdx
index 08a091a..9843948 100644
--- a/units/en/unit8/introduction.mdx
+++ b/units/en/unit8/introduction.mdx
@@ -1,6 +1,8 @@
# Introduction [[introduction]]
-In the last Unit, we learned about Advantage Actor Critic (A2C), a hybrid architecture combining value-based and policy-based methods that help to stabilize the training by reducing the variance with:
+
+
+In Unit 6, we learned about Advantage Actor Critic (A2C), a hybrid architecture combining value-based and policy-based methods that help to stabilize the training by reducing the variance with:
- *An Actor* that controls **how our agent behaves** (policy-based method).
- *A Critic* that measures **how good the action taken is** (value-based method).
@@ -10,13 +12,16 @@ Today we'll learn about Proximal Policy Optimization (PPO), an architecture that
Doing this will ensure **that our policy update will not be too large and that the training is more stable.**
This Unit is in two parts:
-- In this first part, you'll learn the theory behind PPO and use [CleanRL](https://github.com/vwxyzjn/cleanrl) to train your agent on TODO ADD
-- In the second part, we'll get deeper into PPO optimization by using [Sample-Factory](https://samplefactory.dev/).
+- In this first part, you'll learn the theory behind PPO and use [CleanRL](https://github.com/vwxyzjn/cleanrl) to train an agent to learn to jump on platforms.
+- In the second part, we'll get deeper into PPO optimization by using [Sample-Factory](https://samplefactory.dev/) and train an agent playing vizdoom (an open source version of Doom).
-TODO ADD IMAGE TWO PARTS
+
+
+This is the environments you're going to use to train your agents: VizDoom and GodotRL environments
+
-And then, after the theory, we'll train a PPO agent using . TODO ADD
+And then, after the theory, we'll train a PPO agent using CleanRL to jump on platforms.
-TODO: ADD ENVIRONMENTS
+
Sounds exciting? Let's get started! 🚀
+
+
+In Unit 6, we learned about Advantage Actor Critic (A2C), a hybrid architecture combining value-based and policy-based methods that help to stabilize the training by reducing the variance with:
- *An Actor* that controls **how our agent behaves** (policy-based method).
- *A Critic* that measures **how good the action taken is** (value-based method).
@@ -10,13 +12,16 @@ Today we'll learn about Proximal Policy Optimization (PPO), an architecture that
Doing this will ensure **that our policy update will not be too large and that the training is more stable.**
This Unit is in two parts:
-- In this first part, you'll learn the theory behind PPO and use [CleanRL](https://github.com/vwxyzjn/cleanrl) to train your agent on TODO ADD
-- In the second part, we'll get deeper into PPO optimization by using [Sample-Factory](https://samplefactory.dev/).
+- In this first part, you'll learn the theory behind PPO and use [CleanRL](https://github.com/vwxyzjn/cleanrl) to train an agent to learn to jump on platforms.
+- In the second part, we'll get deeper into PPO optimization by using [Sample-Factory](https://samplefactory.dev/) and train an agent playing vizdoom (an open source version of Doom).
-TODO ADD IMAGE TWO PARTS
+
Sounds exciting? Let's get started! 🚀