docs: updates Task documentation

docs/user-guide/Tasks.md

@@ -1,8 +1,8 @@
 # Understanding Tasks
 
-In `pydase`, a task is defined as an asynchronous function without arguments that is decorated with the `@task` decorator and contained in a class that inherits from `pydase.DataService`. These tasks usually contain a while loop and are designed to carry out periodic functions. For example, a task might be used to periodically read sensor data, update a database, or perform any other recurring job.
+In `pydase`, a task is defined as an asynchronous function without arguments that is decorated with the [`@task`][pydase.task.decorator.task] decorator and contained in a class that inherits from [`pydase.DataService`][pydase.DataService]. These tasks usually contain a while loop and are designed to carry out periodic functions. For example, a task might be used to periodically read sensor data, update a database, or perform any other recurring job.
 
-`pydase` allows you to control task execution via both the frontend and Python clients and can automatically start tasks upon initialization of the service. By using the `@task` decorator with the `autostart=True` argument in your service class, `pydase` will automatically start these tasks when the server is started. Here's an example:
+`pydase` allows you to control task execution via both the frontend and Python clients and can automatically start tasks upon initialization of the service. By using the [`@task`][pydase.task.decorator.task] decorator with the `autostart=True` argument in your service class, `pydase` will automatically start these tasks when the server is started. Here's an example:
 
 ```python
 import pydase
@@ -35,4 +35,55 @@ if __name__ == "__main__":
 
 In this example, `read_sensor_data` is a task that continuously reads data from a sensor. By decorating it with `@task(autostart=True)`, it will automatically start running when `pydase.Server(service).run()` is executed.
 
-The `@task` decorator replaces the function with a task object that has `start()` and `stop()` methods. This means you can control the task execution directly using these methods. For instance, you can manually start or stop the task by calling `service.read_sensor_data.start()` and `service.read_sensor_data.stop()`, respectively.
+## Task Lifecycle Control
+
+The [`@task`][pydase.task.decorator.task] decorator replaces the function with a task object that has `start()` and `stop()` methods. This means you can control the task execution directly using these methods. For instance, you can manually start or stop the task by calling `service.read_sensor_data.start()` and `service.read_sensor_data.stop()`, respectively.
+
+## Advanced Task Options
+
+The [`@task`][pydase.task.decorator.task] decorator supports several options inspired by systemd unit services, allowing fine-grained control over task behavior:
+
+- **`autostart`**: Automatically starts the task when the service initializes. Defaults to `False`.
+- **`restart_on_failure`**: Configures whether the task should restart if it exits due to an exception (other than `asyncio.CancelledError`). Defaults to `True`.
+- **`restart_sec`**: Specifies the delay (in seconds) before restarting a failed task. Defaults to `1.0`.
+- **`start_limit_interval_sec`**: Configures a time window (in seconds) for rate limiting task restarts. If the task restarts more than `start_limit_burst` times within this interval, it will no longer restart. Defaults to `None` (disabled).
+- **`start_limit_burst`**: Defines the maximum number of restarts allowed within the interval specified by `start_limit_interval_sec`. Defaults to `3`.
+- **`exit_on_failure`**: If set to `True`, the service will exit if the task fails and either `restart_on_failure` is `False` or the start rate limiting is exceeded. Defaults to `False`.
+
+### Example with Advanced Options
+
+Here is an example showcasing advanced task options:
+
+```python
+import pydase
+from pydase.task.decorator import task
+
+
+class AdvancedTaskService(pydase.DataService):
+    def __init__(self):
+        super().__init__()
+
+    @task(
+        autostart=True,
+        restart_on_failure=True,
+        restart_sec=2.0,
+        start_limit_interval_sec=10.0,
+        start_limit_burst=5,
+        exit_on_failure=True,
+    )
+    async def critical_task(self):
+        while True:
+            raise Exception("Critical failure")
+
+
+if __name__ == "__main__":
+    service = AdvancedTaskService()
+    pydase.Server(service=service).run()
+```
+
+## Key Points
+
+1. **Restart Behavior**: Tasks configured with `restart_on_failure=True` will restart after a failure, subject to the limits specified by `start_limit_interval_sec` and `start_limit_burst`.
+2. **Graceful Shutdown**: Tasks that are manually stopped or cancelled will not trigger restarts.
+3. **Critical Failures**: If `exit_on_failure=True`, the service will terminate if the task fails irrecoverably.
+