Equal-information evaluation of credit assignment in modular financial reinforcement learning
Credit assignment in modular financial reinforcement learning is difficult to evaluate because an attributed signal may improve a component loss without adding decision-relevant information or improving economic outcomes. We propose an evaluation framework combining equal-information reconstruction controls, fixed-utility comparisons and prespecified downstream guardrails. The framework is instantiated in an attribution-guided modular trading architecture and tested in three controlled studies covering update allocation, supervision-target representation and portfolio risk control. We find that residual-based allocation is exactly reproduced by an ordinary loss-budget control under the di...
| Authors | Gennadiy Chistiakov, Yury Yanovich |
|---|---|
| Primary category | cs.AI |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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