This small learning engine is part of a larger project aimed at creating the ultimate Clash Royale bot. The reason I started the project is actually quite funny: my brother Duddy is a pro at the game, and since I couldn't beat him myself, I launched this initiative. Building an engine that could train directly on the actual game was impossible; the game lacks an API and offers no way to speed up gameplay. Consequently, I had to build a complete game simulator that runs faster, making it feasible to train a bot on it.
The link provided points to a "mini" version utilizing components I developed for the project. This version features just one attacking card and one defensive card. You begin by running a series of defensive sequences to see how well you can hold off the attack; then, you let the bot train from scratch and attempt to defend against the attack as effectively as possible. This is a very primitive version of the neural network; while the actual version contains two million parameters, this one has only a few thousand.
As for the project's current status, the simulator is very solid, but I haven't managed to produce a decent bot capable of playing at a human level, let alone at my brother's level. I am not a machine learning expert; I only possess basic knowledge from my CS degree. I received coding assistance from Claude, who acted as my partner in pair programming. Additionally, the game's simulation engine was built primarily by my friend Ambash, who is the project's second major contributor.
The project is MIT Licensed. I would really appreciate some help. Currently, the build process only works on Windows, not Linux. I would also love for someone with extensive machine learning knowledge to review the training and learning mechanism; I’m unsure of its quality right now and whether I’ve made fundamental errors that are ruining the training process every time.
itzikkatz•1h ago
The link provided points to a "mini" version utilizing components I developed for the project. This version features just one attacking card and one defensive card. You begin by running a series of defensive sequences to see how well you can hold off the attack; then, you let the bot train from scratch and attempt to defend against the attack as effectively as possible. This is a very primitive version of the neural network; while the actual version contains two million parameters, this one has only a few thousand.
As for the project's current status, the simulator is very solid, but I haven't managed to produce a decent bot capable of playing at a human level, let alone at my brother's level. I am not a machine learning expert; I only possess basic knowledge from my CS degree. I received coding assistance from Claude, who acted as my partner in pair programming. Additionally, the game's simulation engine was built primarily by my friend Ambash, who is the project's second major contributor.
The project is MIT Licensed. I would really appreciate some help. Currently, the build process only works on Windows, not Linux. I would also love for someone with extensive machine learning knowledge to review the training and learning mechanism; I’m unsure of its quality right now and whether I’ve made fundamental errors that are ruining the training process every time.
https://github.com/itzik123/ClashRoyaleAi