I think any game with strategy is going to have to use reinforcement learning to have any hopes of being understood by its players. Concerns around cheesing strategies can be mitigated for real time games using constraints and cheesing in turn based strategy games is a symptom that maybe the game you're working on isn't that strategic. Ultimately Chess, Go, Dota, Starcraft are compelling games to watch and play becau…
I have a lot of questions for you if you don't mind, maybe you don't want to answer any/all of them that's fine.
1. How do you handle constraining the AI such that it's enjoyable? If you read through the comments here you'll notice just about everyone is saying the hard part is not making a good AI but in making a fun AI. Do you know of any research in this area? most examples I see are all about making the best AI possible. It seems like it's really hard to find a good reward signal to use for "fun". Have you seen this [0,1] paper on learning from human preferences? does Yuri attempt to do something similar? It seems like it would be labor intensive for the play testers.
2. Does Yuri make use of imitation learning at all? I.E learning from lots of human data when it's available to bootstrap the learning process?
3. Do you let the game designers impose any structure on the AI? I.E. "I want three phases for the boss where he progressively gets more aggressive" or use RL just for certain small parts of the behavior? A well defined path finding algorithm with the RL deciding where the agent tries to go? stuff like that?
I have a ton of questions about the technical details side of Yuri too, what frameworks are you using, which RL algorithms etc etc, not sure if you are willing to share those or not.
[0] https://arxiv.org/abs/1706.03741 [1] https://blog.openai.com/deep-reinforcement-learning-from-hum...