diff --git a/examples/training_navigation.py b/examples/training_navigation.py
index 231d4e923a3bf2e7a485b804b19d54a48b67fb2d..18bb63563180c90227e5df12554e3d51faddbc91 100644
--- a/examples/training_navigation.py
+++ b/examples/training_navigation.py
@@ -10,8 +10,8 @@ np.random.seed(1)
 
 # Example generate a rail given a manual specification,
 # a map of tuples (cell_type, rotation)
-transition_probability = [10.0,  # empty cell - Case 0
-                          50.0,  # Case 1 - straight
+transition_probability = [0.5,  # empty cell - Case 0
+                          1.0,  # Case 1 - straight
                           1.0,  # Case 2 - simple switch
                           0.3,  # Case 3 - diamond drossing
                           0.5,  # Case 4 - single slip
@@ -20,8 +20,8 @@ transition_probability = [10.0,  # empty cell - Case 0
                           0.0]  # Case 7 - dead end
 
 # Example generate a random rail
-env = RailEnv(width=5,
-              height=5,
+env = RailEnv(width=7,
+              height=7,
               rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
               number_of_agents=1)
 env_renderer = RenderTool(env)
@@ -49,7 +49,7 @@ def max_lt(seq, val):
 
     idx = len(seq)-1
     while idx >= 0:
-        if seq[idx] < val and seq[idx] > 0:
+        if seq[idx] < val and seq[idx] >= 0:
             return seq[idx]
         idx -= 1
     return None
@@ -110,6 +110,7 @@ for trials in range(1, n_trials + 1):
         eps, action_prob/np.sum(action_prob)),
           end=" ")
     if trials % 100 == 0:
+        action_prob = [1]*4
         print(
             '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
                 env.number_of_agents,