diff --git a/README.md b/README.md
index 3c682cdff80f9b9bc4ac7963f78061c8bcf76e2b..ba8934aea4483662719a1e026be80ddc23e9b306 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