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end-to-end Data parsing / RL / macro tool for Sword Growing (검키우기) game in KakaoTalk Gamebot
RL environment to build agent
Macro support (on Macbook yet)
Preliminaries: uv
Sync your python env
Set rl/config.py
CHAT_OUTPUT_COORD: coordinate of chatbot output
CHAT_INPUT_COORD: coordinate of your input box
Run macro
Functions
F1, F2 is hotkey for "강화", "판매" respectively
F3 starts loop based on AI inference
F4 starts loop based on rule-based strategy
F5 quits program
Used PPO algorithm using SB3
Makefile script for convenient training
Make train: start training
Make test: test 1000 timesteps and plot
Export Kakaotalk chat log
put .csv file to ./data/*
run process_data.ipynb
AI/Heuristic inference support
Trained RL agent is used for inference
Rule-based policy
If LEVEL_THRESHOLD achieved, if fail count surpasses FAIL_COUNT_THRESHOLD, sell current sword
refer to rl/config.py
F1: 강화
F2: 판매
F3: AI inference (loop)
F4: Heuristic inference (loop)
F5: 매크로 종료
Windows support
More training
More data collection (for better environment modeling)
About
카카오톡 검키우기 게임 강화학습 및 매크로
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