So it's tricky. I believe the solution is to break the gameplay down into basic tasks - moving to a targeted point, deciding which point to move to, avoiding at

So it's tricky. I believe the solution is to break the gameplay down into basic tasks - moving to a targeted point, deciding which point to move to, avoiding attacks, attacking, etc. But the more "tasks" you train the RL AI on, its like you put more guardrails on it and it becomes closer and closer to "supervised" learning -- it's only learning what you imagined for it. Which misses out on the fun of a lot of the potential for RL AI, which is that it should come up with novel techniques and strategies to games that humans can learn from. So, yeah. Personally I feel like the answer is doing lots of different versions of mini-training for tons of different games -- but just the sub-part training for the games. Then once it's trained on the "universal mechanics" of gameplay, allow it to start playing more games. The trick is going to be super-generalization. IMHO. But in AI, opinions are a dime a dozen.