Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
This work addresses the challenge of noisy reward signals in financial environments for RL practitioners, representing an incremental improvement in domain-specific methods.
The paper tackles the problem of noisy reward functions in financial trading by introducing a novel reward function that combines imitation learning with reinforcement learning, resulting in improved financial performance metrics compared to traditional benchmarks.
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.