#915 - Created app dirs and set as constants in the top-level init.

- renamed _config_values_main to training_config.py and renamed the ConfigValuesMain class to TrainingConfig.
Moved training_config.py to src/primaite/config/training_config.py
- Renamed all training config yaml file keys to make creating an instance of TrainingConfig easier.
Moved action_type and num_steps over to the training config.
- Decoupled the training config and lay down config.
- Refactored main.py so that it can be ran from CLI and can take a training config path and a lay down config path.
- refactored all outputs so that they save to the session dir.
- Added some necessary setup scripts that handle creating app dirs, fronting example config files to the user, fronting demo notebooks to the user, performing clean-up in between installations etc.
- Added functions that attempt to retrieve the file path of users example config files that have been fronted by the primaite setup.
- Added logging config and a getLogger function in the top-level init.
- Refactored all logs entries logged to use a logger using the primaite logging config.
- Added basic typer CLI for doing things like setup, viewing logs, viewing primaite version, running a basic session.
- Updated test to use new features and config structures.
- Began updating docs. More to do here.

docs/source/session.rst

@@ -29,10 +29,10 @@ the run_generic function should be selected, and should be modified (typically)
 
 .. code:: python
 
-    agent = MyAgent(environment, max_steps)​
-    for episode in range(0, num_episodes):​
-        agent.learn()      ​
-    env.close()​
+    agent = MyAgent(environment, max_steps)
+    for episode in range(0, num_episodes):
+        agent.learn()
+    env.close()
     save_agent(agent)
 
 Where:
@@ -51,29 +51,29 @@ environment is reset between episodes. Note that the example below should not be
 
 .. code:: python
 
-    def learn(self) :​
+    def learn(self) :
 
-    # pre-reqs​​
+    # pre-reqs
 
-    # reset the environment​
-    self.environment.reset()​
-    done = False​
+    # reset the environment
+    self.environment.reset()
+    done = False
 
-    for step in range(max_steps):​
-        # calculate the action​
+    for step in range(max_steps):
+        # calculate the action
         action = ...
 
-        ​# execute the environment step​
-        new_state, reward, done, info = self.environment.step(action)​
+        # execute the environment step
+        new_state, reward, done, info = self.environment.step(action)
 
-        # algorithm updates​
+        # algorithm updates
         ...
 
-        # update to our new state​
-        state = new_state​
+        # update to our new state
+        state = new_state
 
-        # if done, finish episode​
-        if done == True:​
+        # if done, finish episode
+        if done == True:
             break
 
 **Running the session**