Update README.md

README.md

@@ -3,9 +3,10 @@ This repository allows to run Rail Environment multi agent training with the RLL
 It should be clone inside the main flatland repository.
 
 ## Installation:
-`pip install ray`
-
-`pip install gin-config`
+```sh
+    $pip install ray
+    $pip install gin-config
+```
 
 To start a grid search on some parameters, you can create a folder containing a config.gin file (see example in `grid_search_configs/n_agents_grid_search/config.gin`.
 
@@ -14,24 +15,32 @@ Then, you can modify the config.gin file path at the end of the `grid_search_tra
 The results will be stored inside the folder, and the learning curves can be visualized in 
 tensorboard:
 
-```tensorboard --logdir=/path/to/foler_containing_config_gin_file```
+```
+tensorboard --logdir=/path/to/foler_containing_config_gin_file
+```
 
 ## Gin config files
 
 In each config.gin files, all the parameters, except `local_dir` of the `run_experiment` functions have to be specified.
 For example, to indicate the number of agents that have to be initialized at the beginning of each simulation, the following line should be added:
 
-```run_experiment.n_agents = 2```
+```
+run_experiment.n_agents = 2
+```
 
 If several number of agents have to be explored during the experiment, one can pass the following value to the `n_agents` parameter:
 
-```run_experiment.n_agents = {"grid_search": [2,5]}```
+```
+run_experiment.n_agents = {"grid_search": [2,5]}
+```
 
 which is the way to indicate to the tune library to experiment several values for a parameter.
 
 To reference a class or an object within gin, you should first register it from the `train_experiment.py` script adding the following line:
 
-```gin.external_configurable(TreeObsForRailEnv)```
+```
+gin.external_configurable(TreeObsForRailEnv)
+```
 
 and then a `TreeObsForRailEnv` object can be referenced in the `config.gin` file: