PrimAITE/docs/source/primaite_session.rst

.. only:: comment

    © Crown-owned copyright 2023, Defence Science and Technology Laboratory UK

.. _run a primaite session:

.. admonition:: Deprecated
   :class: deprecated

   PrimAITE Session is being deprecated in favour of Jupyter Notebooks. The ``session`` command will be removed in future releases, but example notebooks will be provided to demonstrate the same functionality.

Run a PrimAITE Session
======================

``PrimaiteSession`` allows the user to train or evaluate an RL agent on the primaite simulation with just a config file,
no code required. It manages the lifecycle of a training or evaluation session, including the setup of the environment,
policy, simulator, agents, and IO.

If you want finer control over the RL policy, you can interface with the :py:module::`primaite.session.environment`
module directly without running a session.



Run
---

A PrimAITE session can be started either with the ``primaite session`` command from the cli
(See :func:`primaite.cli.session`), or by calling :func:`primaite.main.run` from a Python terminal or Jupyter Notebook.

There are two parameters that can be specified:
  - ``--config``: The path to the config file to use. If not specified, the default config file is used.
  - ``--agent-load-file``: The path to the pre-trained agent to load. If not specified, a new agent is created.

Outputs
-------

Running a session creates a session output directory in your user data folder. The filepath looks like this:
``~/primaite/3.0.0/sessions/YYYY-MM-DD/HH-MM-SS/``. This folder contains the simulation sys logs generated by each node,
the saved agent checkpoints, and final model. The folder also contains a .json file for each episode step that
contains the action, reward, and simulation state. These can be found in
``~/primaite/3.0.0/sessions/YYYY-MM-DD/HH-MM-SS/simulation_output/episode_<n>/step_metadata/step_<n>.json``