Robot See, Robot Do: Imitation Reward for Noisy Financial Environments

TL;DR

This paper proposes a robust imitation-based reward function for reinforcement learning in noisy financial markets, improving policy learning and financial performance by combining expert feedback with reinforcement signals.

Contribution

It introduces a novel reward function that integrates imitation learning with RL to better handle reward noise in financial trading environments.

Findings

Improved financial performance metrics over benchmarks.

Enhanced policy robustness in noisy environments.

Effective integration of imitation feedback with reinforcement learning.

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.