diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index 9e6d80fa999c0b675af11537ad5aa8edb8dff008..6da1ae4133c8e76ff8328bb90e535c68e470d1a7 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -123,15 +123,15 @@ We start by importing the necessary Flatland libraries
 
 .. code-block:: python
 
-from flatland.envs.generators import complex_rail_generator
-from flatland.envs.rail_env import RailEnv
+    from flatland.envs.generators import complex_rail_generator
+    from flatland.envs.rail_env import RailEnv
 
 The complex_rail_generator is used in order to guarantee feasible railway network configurations for training.
 Next we configure the difficulty of our task by modifying the complex_rail_generator parameters.
 
 .. code-block:: python
 
-env = RailEnv(width=15,
+    env = RailEnv(width=15,
               height=15,
               rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0),
               number_of_agents=5)
@@ -145,7 +145,7 @@ cover the whole spectrum from easy to complex tasks.
 Once we are set with the environment we can load our preferred agent from either RLlib or any other ressource. Here we use a random agent to illustrate the code.
 .. code-block:: python
 
-agent = RandomAgent(env., 4)
+    agent = RandomAgent(env., 4)