#2257: massive docs addition for config file

docs/source/configuration/training_config.rst

@@ -4,6 +4,22 @@
 
 ``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``
 ----------------
@@ -23,3 +39,37 @@ 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.