#2374 Remove primaite session

docs/source/configuration/io_settings.rst

@@ -13,42 +13,12 @@ This section configures how PrimAITE saves data during simulation and training.
 .. code-block:: yaml
 
     io_settings:
-        save_final_model: True
-        save_checkpoints: False
-        checkpoint_interval: 10
         # save_logs: True
-        # save_transactions: False
         save_agent_actions: True
         save_step_metadata: False
         save_pcap_logs: False
         save_sys_logs: False
 
-``save_final_model``
---------------------
-
-Optional. Default value is ``True``.
-
-Only used if training with PrimaiteSession.
-If ``True``, the policy will be saved after the final training iteration.
-
-
-``save_checkpoints``
---------------------
-
-Optional. Default value is ``False``.
-
-Only used if training with PrimaiteSession.
-If ``True``, the policy will be saved periodically during training.
-
-
-``checkpoint_interval``
------------------------
-
-Optional. Default value is ``10``.
-
-Only used if training with PrimaiteSession and if ``save_checkpoints`` is ``True``.
-Defines how often to save the policy during training.
-
 
 ``save_logs``
 -------------

docs/source/configuration/training_config.rst

@@ -1,75 +0,0 @@
-.. only:: comment
-
-    © Crown-owned copyright 2023, Defence Science and Technology Laboratory UK
-
-``training_config``
-===================
-Configuration items relevant to how the Reinforcement Learning agent(s) will be trained.
-
-``training_config`` hierarchy
------------------------------
-
-.. code-block:: yaml
-
-    training_config:
-        rl_framework: SB3 # or RLLIB_single_agent or RLLIB_multi_agent
-        rl_algorithm: PPO # or A2C
-        n_learn_episodes: 5
-        max_steps_per_episode: 200
-        n_eval_episodes: 1
-        deterministic_eval: True
-        seed: 123
-
-
-``rl_framework``
-----------------
-The RL (Reinforcement Learning) Framework to use in the training session
-
-Options available are:
-
-- ``SB3`` (Stable Baselines 3)
-- ``RLLIB_single_agent`` (Single Agent Ray RLLib)
-- ``RLLIB_multi_agent`` (Multi Agent Ray RLLib)
-
-``rl_algorithm``
-----------------
-The Reinforcement Learning Algorithm to use in the training session
-
-Options available are:
-
-- ``PPO`` (Proximal Policy Optimisation)
-- ``A2C`` (Advantage Actor Critic)
-
-``n_learn_episodes``
---------------------
-The number of episodes to train the agent(s).
-This should be an integer value above ``0``
-
-``max_steps_per_episode``
--------------------------
-The number of steps each episode will last for.
-This should be an integer value above ``0``.
-
-
-``n_eval_episodes``
--------------------
-Optional. Default value is ``0``.
-
-The number of evaluation episodes to run the trained agent for.
-This should be an integer value above ``0``.
-
-``deterministic_eval``
-----------------------
-Optional. By default this value is ``False``.
-
-If this is set to ``True``, the agents will act deterministically instead of stochastically.
-
-
-
-``seed``
---------
-Optional.
-
-The seed is used (alongside ``deterministic_eval``) to reproduce a previous instance of training and evaluation of an RL agent.
-The seed should be an integer value.
-Useful for debugging.