Update README.md

README.md

@@ -94,14 +94,16 @@ flit install --symlink
 
 Find ``README.md`` in the corresponding directory of the algorithm you want to use and follow the instructions. For example, to train DreamerV3 agents, use
 
-```
+```bash
 bash train_dm3.sh 2000 0 --task carla_four_lane --dreamerv3.logdir ./logdir/carla_four_lane
 ```
 
 The command will launch CARLA at 2000 port, load task a built-in task named `carla_four_lane`, and start the visualization tool at port 9000 (2000+7000) which can be accessed through `http://localhost:9000/`. You can append flags to the command to overwrite yaml configurations.
 
 ### Creating Tasks
 
+The section explains how to create CarDreamer tasks in a standalone mode without loading our integrated models. This can be helpful if you want to train and evaluate your own models other than our integrated DreamerV2 and DreamerV3 on CarDreamer tasks.
+
 CarDreamer offers a range of built-in task classes, which you can explore in the [CarDreamer Docs: Tasks and Configurations](https://car-dreamer.readthedocs.io/en/latest/tasks.html#tasks-and-environments).
 
 Each task class can be instantiated with various configurations. For instance, the right-turn task can be set up with simple, medium, or hard settings. These settings are defined in YAML blocks within [tasks.yaml](https://github.com/ucd-dare/CarDreamer/blob/master/car_dreamer/configs/tasks.yaml). The task creation API retrieves the given identifier (e.g., `carla_four_lane_hard`) from these YAML task blocks and injects the settings into the task class to create a gym task instance.