https://github.com/DevJac/solve_the_spireI stretched the truth a bit, I'm actually doing something like "hierarchical model-free reinforcement learning", even so, figuring out how to break the game down to create a hierarchy of agents is a lot of work. Basically, the AI is composed of about 8 different traditional RL agents (neural networks), each deciding a different thing. One chooses which cards to draft, one chooses which actions to take in combat, one chooses which path to take on the map, etc.
Simple rules like "play random cards until your energy is used up" alone can sometimes beat the act 1 boss. My AI is barely above that, and still far from solving the game. I'm not convinced even DeepMind or other researchers could solve Slay the Spire right now.
It shows definite signs of improvement, but has only reached a point where it can beat the act 1 boss about 50% of the time. I think that is its limit right now. I'm doing policy gradient which is very sample inefficient. I'm going to implement soft-actor-critic and see if it can do better with better sample efficiency.
One thing I like about Slay the Spire is it's an environment to solve, not a competition. Gamers like to talk about PvP and PvE, well, I prefer AI vs environment over AI vs AI. In the end, an AI will win the competition, no surprise. An AI solving a new kind of environment is much more exciting IMHO.