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In 2014, I wrote a minimax-based AI to play games on Pokemon Showdown. We adapted Showdown's battle simulator for our tree search. The hardest part was syncing
by varunramesh 8y ago
In 2014, I wrote a minimax-based AI to play games on Pokemon Showdown. We adapted Showdown's battle simulator for our tree search. The hardest part was syncing the local simulator state with the actual game state - the battle state in Pokemon is both complex and partially observed. Bugs in this process could result in the AI using Protect twice because the state wasn't updated with the fact that it used Protect the previous turn.
The minimax AI was able to use tactics like Pain Split, Spikes, and Magic Guard.
Writeup: https://varunramesh.net/content/documents/cs221-final-report.pdf https://varunramesh.net/content/documents/cs221-final-report...
GitHub: https://github.com/rameshvarun/showdownbot https://github.com/rameshvarun/showdownbot
- brandonhorst 8y agoConsidered building something like this last year, and couldn't find any prior art online. Looking forward to reading your report!
- catwell 8y agoThis is super interesting. I would like to see a real (learning, à la AlphaGo) A.I. play on Showdown OU someday, without cheating (i.e. about the number of matches per day a real human player would play). I think there are a few challenges not found in most other online games, one being that strategies that win on different strata of the ladder are not the same (e.g. hyper offense is the most efficient on the lower ladder, but at some point around 1500 / 1600 you will start losing using it...). Also, I wonder how well an A.I. trained on the ladder would do in a tournament (e.g. SPL), where the metagame is a bit different. Your code looks like a good entry point for that, all that's needed is to write a new bot using ML libraries...