From 9859da462aed39ebca596614b4d33dce36b32476 Mon Sep 17 00:00:00 2001
From: Adrien <adrien.payen@student.uclouvain.be>
Date: Sun, 19 May 2024 11:17:09 +0200
Subject: [PATCH] update readme

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

diff --git a/README.md b/README.md
index 3c682cd..ba8934a 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ The **tmc.py**  file defines a **class TransitionMatrixCalculator** that calcula
  
 ### 2. markovDecison.py
  
-The **markovDecison.py**  file contains a **class MarkovDecisionProcess** that defines the Value Iteration algorithms for the different strategies. The **solve** function is used to calculate the optimal policy by the Value Iteration algorithm. This is achieved by using the 3 functions **_compute_vi_safe**,*_compute_vi_normal**,**_compute_vi_risky**  which make it possible to calculate the Value Iteration for each of the dice and choose the minimum of all the values. Then, the **markovDecision** function using the **solve** function displays the optimal strategy (the dice to be played according to a layout) and the theoretical cost of each square according to a cyclic or acyclic game.
+The **markovDecison.py**  file contains a **class MarkovDecisionProcess** that defines the Value Iteration algorithms for the different strategies. The **solve** function is used to calculate the optimal policy by the Value Iteration algorithm. This is achieved by using the 3 functions **_compute_vi_safe**, **_compute_vi_normal**, **_compute_vi_risky**  which make it possible to calculate the Value Iteration for each of the dice and choose the minimum of all the values. Then, the **markovDecision** function using the **solve** function displays the optimal strategy (the dice to be played according to a layout) and the theoretical cost of each square according to a cyclic or acyclic game.
  
 ### 3. validation.py
  
-- 
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