Delivering Personalised Gameplay Through ML-Agents, Approaches

Hi,

I work with an educational games startup, and we have been thinking about ways by which we can deliver performance-based content to our users. Reinforcement learning in ML-Agents appears to be a good fit for this, as the agent could suggest certain problems to the user, and be punished or rewarded accordingly based upon whether or not the user is correct. In theory, this could enable the game to adapt to how the user is playing, by trying to maximise reward with a certain player without incurring punishment.

Now if it were possible to train an ML-Agent using reinforcement learning on-device, during gameplay, adjust the weights dynamically and persist the model, this would be ideal. However, it seems like the only way to train an agent is through a Unity instance coupled to a Python environment. Is this correct? If so:

1. Are there any ways to collect data from players and train later? In essence, to provide a minimally-trained model to the user at the start, retrain it (possibly each day based upon their playing data), and then deliver a retrained model back to them? It appears that the training in Python is coupled to sensors in the Unity editor environment?

2. Failing that, are there any means by which multiple Unity instances (probably through WebGL) can be linked to a single Python instance? I understand that this would limit the degree of personalisation of our model, but it may be an interesting experiment.

Of course, ML-Agents may not be the best way to solve this problem, and I am open to any other suggestions on how you would approach this.

 

At the moment there is no way to do this other than from the editor with demonstration files. This something we are thinking about but we don't have a short term solution for collecting remote data from player sessions.

We don't officially support WebGL with ML-Agents. If you were to change the code yourself you could probably get it to work, but it might take a significant amount of changes.