dlb-rl/pulse-rl
Code for PulseRL: Enabling Offline Reinforcement Learning for Digital Marketing Systems via Conservative Q-Learning.
PulseRL helps digital marketing managers and strategists improve campaign performance using historical campaign data. It takes your past campaign interaction data (like clicks, views, and conversions) and uses it to recommend optimal strategies for future campaigns, aiming to maximize engagement and ROI. This tool is designed for professionals in digital advertising or e-commerce who want to refine their targeting and content delivery without running live, risky A/B tests.
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Use this if you have a wealth of historical digital marketing data and want to develop more effective campaign strategies without directly experimenting on live audiences.
Not ideal if you need a tool for real-time campaign optimization or if you don't have a substantial dataset of past marketing interactions.
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Language
Python
License
MIT
Category
Last pushed
Sep 08, 2021
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