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

Reinforcement learning (RL) for foreign exchange (Forex) trading requires realistic execution modeling, interpretable reward design, and action spaces that respect market constraints. Existing approaches often simplify these aspects, limiting policy transparency and practical relevance. This paper p