Changes to the documentation

docs/gettingstarted.rst

@@ -106,11 +106,16 @@ following code. Also, tree observation data is displayed by RenderTool by defaul
 .. 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)
+        env.obs_builder.util_print_obs_subtree(
+                tree=obs[i], 
+                num_features_per_node=5
+                )
 
 The complete code for this part of the Getting Started guide can be found in 
-examples/simple_example_1.py, examples/simple_example_2.py and 
-examples/simple_example_3.py
+
+* `examples/simple_example_1.py <https://gitlab.aicrowd.com/flatland/flatland/blob/master/examples/simple_example_1.py>`_
+* `examples/simple_example_2.py <https://gitlab.aicrowd.com/flatland/flatland/blob/master/examples/simple_example_2.py>`_
+* `examples/simple_example_3.py <https://gitlab.aicrowd.com/flatland/flatland/blob/master/examples/simple_example_3.py>`_
 
 
 
@@ -119,7 +124,7 @@ Part 2 : Training a Simple an Agent on Flatland
 This is a brief tutorial on how to train an agent on Flatland.
 Here we use a simple random agent to illustrate the process on how to interact with the environment.
 The corresponding code can be found in examples/training_example.py and in the baselines repository
-you find a tutorial to train a DQN agent to solve the navigation task.
+you find a tutorial to train a `DQN <https://arxiv.org/abs/1312.5602>`_ agent to solve the navigation task.
 
 We start by importing the necessary Flatland libraries
 
@@ -133,12 +138,17 @@ Next we configure the difficulty of our task by modifying the complex_rail_gener
 
 .. code-block:: python
 
-    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)
+    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)
               
-The difficulty of a railway network depends on the dimensions (width x height) and the number of agents in the network.
+The difficulty of a railway network depends on the dimensions (`width` x `height`) and the number of agents in the network.
 By varying the number of start and goal connections (nr_start_goal) and the number of extra railway elements added (nr_extra)
 the number of alternative paths of each agents can be modified. The more possible paths an agent has to reach its target the easier the task becomes.
 Here we don't specify any observation builder but rather use the standard tree observation. If you would like to use a custom obervation please follow
@@ -214,10 +224,10 @@ A RailEnv environment can then be created as usual:
                   obs_builder_object=CustomObs())
 
 As for generating custom rail maps, the RailEnv class accepts a rail_generator 
-argument that must be a function with arguments 'width', 'height', 'num_agents', 
-and 'num_resets=0', and that has to return a GridTransitionMap object (the rail map),
+argument that must be a function with arguments `width`, `height`, `num_agents`, 
+and `num_resets=0`, and that has to return a GridTransitionMap object (the rail map),
 and three lists of tuples containing the (row,column) coordinates of each of 
-num_agent agents, their initial orientation (0=North, 1=East, 2=South, 3=West), 
+num_agent agents, their initial orientation **(0=North, 1=East, 2=South, 3=West)**, 
 and the position of their targets.
 
 For example, the following custom rail map generator returns an empty map of