Deep Q-Network Interpretability: Applications to ETF Trading

Abstract

We present an interpretability infrastructure for Reinforcement Learning (RL) based trading strategies. For all audiences to be able to answer the question of 'how does the algorithm work?', we provide a visual and user-friendly approach, in contrast to a more quantitative approach. This allows not only a technical audience to consume insights derived from an RL-based trading approach. In this application, we introduce a three module approach in understanding value-based RL, specifically Deep Q-Learning. We demonstrate this infrastructure and possible derived outcomes of using this infrastructure when applied to trading a market ETF in a given time interval.