#915 - Refactored documentation and included APi docs, dependencies.

- make files now re-build autosummary and deps file.
- Added typer and platformdirs to deps in pyproject.toml.
- Made root_is_pure = True in setup.py as platform/python specific wheels don't need to be built but the option is there should we need to.
-
Added an e2e test for primaite.main.run func.

docs/source/custom_agent.rst

@@ -0,0 +1,65 @@
+Custom Agents
+=============
+
+
+**Integrating a user defined blue agent**
+
+Integrating a blue agent with PrimAITE requires some modification of the code within the main.py file. The main.py file
+consists of a number of functions, each of which will invoke training for a particular agent. These are:
+
+* Generic (run_generic)
+* Stable Baselines 3 PPO (:func:`~primaite.main.run_stable_baselines3_ppo)
+* Stable Baselines 3 A2C (:func:`~primaite.main.run_stable_baselines3_a2c)
+
+The selection of which agent type to use is made via the training config file. In order to train a user generated agent,
+the run_generic function should be selected, and should be modified (typically) to be:
+
+.. code:: python
+
+    agent = MyAgent(environment, num_steps)
+    for episode in range(0, num_episodes):
+        agent.learn()
+    env.close()
+    save_agent(agent)
+
+Where:
+
+* *MyAgent* is the user created agent
+* *environment* is the :class:`~primaite.environment.primaite_env.Primaite` environment
+* *num_episodes* is the number of episodes in the session, as defined in the training config file
+* *num_steps* is the number of steps in an episode, as defined in the training config file
+* the *.learn()* function should be defined in the user created agent
+* the *env.close()* function is defined within PrimAITE
+* the *save_agent()* assumes that a *save()* function has been defined in the user created agent. If not, this line can
+  be ommitted (although it is encouraged, since it will allow the agent to be saved and ported)
+
+The code below provides a suggested format for the learn() function within the user created agent.
+It's important to include the *self.environment.reset()* call within the episode loop in order that the
+environment is reset between episodes. Note that the example below should not be considered exhaustive.
+
+.. code:: python
+
+    def learn(self) :
+
+    # pre-reqs
+
+    # reset the environment
+    self.environment.reset()
+    done = False
+
+    for step in range(max_steps):
+        # calculate the action
+        action = ...
+
+        # execute the environment step
+        new_state, reward, done, info = self.environment.step(action)
+
+        # algorithm updates
+        ...
+
+        # update to our new state
+        state = new_state
+
+        # if done, finish episode
+        if done == True:
+            break