1. We use a couple of tricks for this, there's ideas from human preferences. There's modeling a reward function to maximize fun instead of pure performance, there's handicaps and a host of other tricks. Different tricks work better for different games but game developers can easily configure them

2. It can, imitation learning turns RL into a supervised learning problem though from my experience it's not really needed. It's more of a if you already have it then you can make the training a bit faster

3. Yes all the RL part becomes tuning a config file where the game developer needs to describe the task it wants to be accomplished

Regarding technical details, the infra is agnostic to the specific deep learning library you'd like to use and the algorithms are chosen based on the types of inputs (continuous vs discrete), size of the space (the end all of strategy games or a casual puzzle game) and type of game (full information vs partial information).