diff --git a/units/en/unit6/conclusion.mdx b/units/en/unit6/conclusion.mdx
index 68393d7..3da4332 100644
--- a/units/en/unit6/conclusion.mdx
+++ b/units/en/unit6/conclusion.mdx
@@ -4,7 +4,7 @@ Congrats on finishing this unit and the tutorial. You've just trained your first
 
 **Take time to grasp the material before continuing**. You can also look at the additional reading materials we provided in the *additional reading* section.
 
-Feel free to train your agent in other environments. The **best way to learn is to try things on your own!** For instance, what about teaching your robot [to stack objects](https://panda-gym.readthedocs.io/en/latest/usage/environments.html#sparce-reward-end-effector-control-default-setting)?
+Feel free to train your agent in other environments. The **best way to learn is to try things on your own!** For instance, what about teaching your robotic arm [to stack objects](https://panda-gym.readthedocs.io/en/latest/usage/environments.html#sparce-reward-end-effector-control-default-setting) or slide objects?
 
 In the next unit, we will learn to improve Actor-Critic Methods with Proximal Policy Optimization using the [CleanRL library](https://github.com/vwxyzjn/cleanrl). Then we'll study how to speed up the process with the [Sample Factory library](https://samplefactory.dev/). You'll train your PPO agents in these environments: VizDoom, Racing Car, and a 3D FPS.