From a12812a436777b24f648ef9b84b11e463e385282 Mon Sep 17 00:00:00 2001
From: number_cruncher <lennart.oestreich@stud.tu-darmstadt.de>
Date: Sun, 16 Mar 2025 19:09:27 +0100
Subject: [PATCH] graphic

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 92e7ade..bedab60 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ The REINFORCE algorithm (also known as Vanilla Policy Gradient) is a policy grad
 
 > 🛠 **To be handed in**
 > Use PyTorch to implement REINFORCE and solve the CartPole environement. Share the code in `reinforce_cartpole.py`, and share a plot showing the total reward accross episodes in the `README.md`. Also, share a file `reinforce_cartpole.pth` containing the learned weights. For saving and loading PyTorch models, check [this tutorial](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference)
-![REINFORCE CartPole](reinforce_cartpole_dr_0.5.png)
+![](reinforce_cartpole_dr_0.5.png)
 
 
 ## Model Evaluation
-- 
GitLab