Article

Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading

2026-03-23

Abstract excerpt

Applying reinforcement learning (RL) to foreign exchange (Forex) trading remains challenging because realistic environments, well-defined reward functions, and expressive action spaces are all required simultaneously. Many existing studies simplify these elements through basic simulators, single scalar rewards, and limited action representations, making learned policies difficult to diagnose and limiting practical...

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Literature Corpus work: 46cc4db0-3f9d-56f1-b970-8f116809fefa
DOI: 10.20944/preprints202603.1701.v1

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