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About this paper
Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, Ronnie Sircar and 1 more
The authors develop a paycheck optimization framework that learns to divide income among debts, savings, and retirement goals as their remaining amounts change. It assigns each goal a preference-weighted utility and uses deep deterministic policy gradient to learn allocations. In constant-rate simulations of three constructed profiles, allocations reflected assigned priorities and completed configured goals; this is not evidence of real-world performance.
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