diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index 0b7c552f755bcf1bc650f93d9abceaf84056d884..44741ff43b052d616747ade4572f9fe9c51fd6f9 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -74,7 +74,7 @@ generated rail networks).
 
 Environments can be rendered using the utils.rendertools utilities, for example:
 
-.. code-block: python
+.. code-block:: python
 
     env_renderer = RenderTool(env, gl="QT")
     env_renderer.renderEnv(show=True)
@@ -85,7 +85,7 @@ with a dictionary of actions whose keys are agents' handles (returned by
 env.get_agent_handles() ) and the corresponding values the selected actions.
 For example, for a 2-agents environment:
 
-.. code-block: python
+.. code-block:: python
 
     handles = env.get_agent_handles()
     action_dict = {handles[0]:0, handles[1]:0}
@@ -97,6 +97,16 @@ status for each agent. Further, the 'dones' dictionary returns an extra key
 '__all__' that is set to True after all agents have reached their goals.
 
 
+In the specific case a TreeObsForRailEnv observation builder is used, it is 
+possible to print a representation of the returned observations with the 
+following code. Also, tree observation data is displayed by RenderTool by default.
+
+.. code-block:: python
+
+    for i in range(env.get_num_agents()):
+        env.obs_builder.util_print_obs_subtree(tree=obs[i], num_features_per_node=5)
+
+
 
 Part 2 : Training a Simple DQN Agent
 --------------