Adding new training to finished brains and will they keep learning in playable game?

I am interested in 2 things on how this works.

If I train a brain to do a simple task like pick up object or something and finish and then decide to add actions to that same brain and train it again does it still know what it learned since last time? Can i for example teach it to pick up, ok finish, then add eat and then add mining etc to improve it. Or better yet teach it each of those individually in environments designed to those specific tasks and then merge them all together?

Also if I make a game with something like this and people play my game, will the final trained brain keep on learning when the game is being played or will the brain just keep its functionality that it got at the end of the training I did for it while in the unity editor?

The short answer to your first question is no. You would need to have all of your observations and actions setup initially and do something like curriculum learning in order to ramp up learning over time.

The answer to your second question is also no. A trained and deployed brain does not learn. It is essentially "frozen" once you are done training it.

in the future, it will be impossible to "finish off" the brain?

Does this option not help?
--resume: If set, the training code loads an already trained model to initialize the neural network before training. The learning code looks for the model in models// (which is also where it saves models at the end of training). This option only works when the models exist, and have the same behavior names as the current agents in your scene.

You can resume a training session, which is different than a brain learning that is deployed in a game. There is currently no support for 'online' learning in ml-agents.