Artificial Intelligence

    

Neuro-symbolic Meta Reinforcement Learning for Trading

Authors: S. I. Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig

We model short-duration (e.g. day) trading in financial mar- kets as a sequential decision-making problem under uncer- tainty, with the added complication of continual concept- drift. We therefore employ meta reinforcement learning via the RL2 algorithm. It is also known that human traders often rely on frequently occurring symbolic patterns in price series. We employ logical program induction to discover symbolic patterns that occur frequently as well as recently, and ex- plore whether using such features improves the performance of our meta reinforcement learning algorithm. We report ex- periments on real data indicating that meta-RL is better than vanilla RL and also benefits from learned symbolic features.

Comments: 4 Pages. Accepted at Muffin@AAAI'23

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Submission history

[v1] 2023-02-10 02:10:49

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