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PrimAITE/docs/source/primaite_session.rst

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© 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/{VERSION}/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/{VERSION}/sessions/YYYY-MM-DD/HH-MM-SS/simulation_output/episode_<n>/step_metadata/step_<n>.json``