Update README.md

README.md

@@ -14,31 +14,31 @@ 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:
 
-`
+```
 run_experiment.obs_builder = {"grid_search": [@TreeObsForRailEnv(), @GlobalObsForRailEnv()]}
 TreeObsForRailEnv.max_depth = 2
-`
+```
 
 Note that `@TreeObsForRailEnv` references the class, while `@TreeObsForRailEnv()` references instantiates an object of this class.