ロボットが見て、ロボットが行う：ノイズの多い金融環境における模倣報酬
Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
金融資産取引の強化学習において、トレンドラベリングアルゴリズムを専門家とした模倣学習を活用し、ノイズの多い報酬信号に頑健な報酬関数を提案した研究。
著者: Sven Goluža, Tomislav Kovačević, Stjepan Begušić, Zvonko Kostanjčar
分類: cs.LG, cs.RO, q-fin.TR
原文アブストラクト
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.