.. _tutorial_agent_task_numbers:


Changing Agent/Task Numbers
========================================

**Prerequisite**: :ref:`tutorial_dynamic_task`







The scenarios discussed so far involve an *MT-SR (Multiple Tasks - Single Robot)* scenario where individual agents are supposed to handle multiple tasks sequentially. Now, based on the configuration file used in :ref:`tutorial_dynamic_task`, let's modify the settings to create an *ST-MR (Single Task - Multiple Robots)* scenario, where multiple agents collaborate on a single task.  

.. code-block:: yaml

    agents:
      behavior_tree_xml: default_bt.xml
      quantity: 400
      ...
      communication_radius: 150
      ...

    tasks:
      quantity: 10
      ...
      amounts:
        min: 100.0
        max: 6000.0
      dynamic_task_generation:
        enabled: True
        interval_seconds: 1000
        max_generations: 2
        tasks_per_generation: 5

First, update some parameters for agents and tasks: 

- The number of agents is increased to 400. 
- As there will be more agents than before, let us reduce the communication range of each agent to 150 (see :ref:`Configuration Guide: agents <config_guide_agents>`). 
- The number of tasks starts at 10 and increases by 5 tasks every 1000 seconds for a total of 2 additional generations. 
- Since multiple agents will collaborate on a single task, let's adjust the ``tasks.amounts`` range to `[100, 6000]` to accommodate the increased workload of individual tasks (see :ref:`Configuration Guide: tasks <config_guide_tasks>`). 


.. code-block:: yaml

    simulation:
      sampling_freq: 1.0
      ...
      task_visualisation_factor: 100

      rendering_options: # Only works if `rendering_mode` is `Screen`
        agent_tail: True
        agent_communication_topology: True
        agent_situation_awareness_circle: False
        agent_id: False
        agent_work_done: False
        agent_assigned_task_id: False
        agent_path_to_assigned_tasks: False
        task_id: False


Also, modify some other parameters related to simulation rendering:

- Modify the ``simulation.task_visualisation_factor`` to 100 to reduce the size of task visualizations on the screen (See :ref:`Configuration Guide: simulation <config_guide_simulation>`). 
- Since having a large number of agents can cause the screen to become overcrowded and difficult to interpret during rendering, set most rendering options to ``False``, except for ``agent_tail`` and ``agent_communication_topology``.

.. code-block:: yaml

    decision_making:
      plugin: plugins.grape.grape.GRAPE
      GRAPE:
        cost_weight_factor: 1.0
        social_inhibition_factor: 1
        ...

Lastly, adjust the GRAPE settings:

- The current implementation of GRAPE includes the ``social_inhibition_factor`` to address MT-SR scenarios. For MT-SR scenarios we have addressed in the previous tutorials, this value was set to 100. But now, for this ST-MR scenario, let us set ``social_inhibition_factor`` to 1 as we need not to hinder the agents from collaborating with each other (see :ref:`Decision-Making Plugins: GRAPE <plugin_grape>`).


Reflecting the above modification, save the configuraiton file as ``config_grape.yaml``. Then, let's execute the simulator with the configuration file as follows:

.. code-block:: shell

   python main.py --config=config_grape.yaml

Then, you will see a simulation similar to the following.   

.. list-table::
   :widths: 50 50
   :header-rows: 0

   * - .. figure:: result/GRAPE_STMR/GRAPE_a400_t10_2024-08-21_16-51-26.gif
         :width: 93%
         :align: center

      

     - .. figure:: result/GRAPE_STMR/GRAPE_a400_t10_2024-08-21_16-51-26_timewise.png
         :width: 100%
         :align: center



See the configuration file used for the above simulation: :download:`config_grape.yaml <result/GRAPE_STMR/GRAPE_a400_t10_2024-08-21_16-51-26.yaml>`.