Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
ArXiv ID: 2411.08637 “View on arXiv”
Authors: Unknown
Abstract
The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function, which hinders effective policy learning by RL agents. Given the critical importance of reward function design in RL problems, this paper introduces a novel and more robust reward function by leveraging imitation learning, where a trend labeling algorithm acts as an expert. We integrate imitation (expert’s) feedback with reinforcement (agent’s) feedback in a model-free RL algorithm, effectively embedding the imitation learning problem within the RL paradigm to handle the stochasticity of reward signals. Empirical results demonstrate that this novel approach improves financial performance metrics compared to traditional benchmarks and RL agents trained solely using reinforcement feedback.
Keywords: Reinforcement Learning (RL), Imitation Learning, Reward Function Design, Model-free RL, Asset Trading, Equities
Complexity vs Empirical Score
- Math Complexity: 7.0/10
- Empirical Rigor: 6.0/10
- Quadrant: Holy Grail
- Why: The paper integrates advanced RL/IL concepts with formal MDP definitions and algorithmic modifications, reflecting high math complexity. Empirical results on intraday futures data and risk-adjusted metrics show backtest-ready implementation and data-heavy validation.
flowchart TD
A["Research Goal: <br>Robust RL for <br>Noisy Financial Markets"] --> B["Methodology: <br>Imitation-Enhanced Reward <br>(Expert + RL Feedback)"]
B --> C["Input: <br>Asset Trading Data <br>(Equities)"]
C --> D["Computational Process: <br>Model-Free RL Algorithm <br>using Hybrid Reward Signal"]
D --> E["Outcome: <br>Improved Financial Metrics <br>vs. Benchmarks"]