From 6280a9bef93d93e67b916353aaa0f798ddf7b854 Mon Sep 17 00:00:00 2001
From: Simone Reynoso Donzelli <sreynosodonzelli@uwaterloo.ca>
Date: Sat, 16 Nov 2024 17:05:36 -0500
Subject: [PATCH] ok

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4029419..0a5de57 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
 # A Reinforcement Learning Approach with Masked Agents for Chemical Process Flowsheet Design
 
 This repository contains the code for the two case studies presented in the paper *A reinforcement learning approach with masked agents for chemical process flowsheet design*. 
-The work focuses on the generation, design and optimization of chemical process flowsheets using Reinforcement Learning. The full paper can be found in <https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.18584?af=R>
+The work focuses on the generation, design and optimization of chemical process flowsheets using Reinforcement Learning. 
+
+The full paper can be found in <https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.18584?af=R>
 
 ## Case Study 1
 This case study compares the performance of discrete and hybrid masked PPO agents in generating a chemical process flowsheet for the reaction $A \rightarrow B$. With this illustrative example it was found that for simple examples in which the number of discrete and continuous varibles are reduced, a fully discretized agent outperfroms the hybrid agent, achieving better rewards. 
-- 
GitLab