Updated predictor to respect differential speed in env.

examples/training_example.py

@@ -10,7 +10,6 @@ np.random.seed(1)
 
 # Use the complex_rail_generator to generate feasible network configurations with corresponding tasks
 # Training on simple small tasks is the best way to get familiar with the environment
-#
 
 TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
 LocalGridObs = LocalObsForRailEnv(view_height=10, view_width=2, center=2)