IMPROVING DEEP REINFORCEMENT LEARNING FOR FINANCIAL TRADING USING NEURAL NETWORK DISTILLATION

Deep Reinforcement Learning (RL) is increasingly used for developing financial trading agents for a wide range of tasks. However, optimizing deep RL agents is known to be notoriously difficult and unstable, hindering the performance of financial trading agents. In this work, we propose a novel method for training deep RL agents, leading to better performing and more efficient RL agents. The proposed method works by first training a large and complex deep RL agent and then transferring the knowledge into a smaller and more efficient agent using neural network distillation. The ability of the proposed method to significantly improve deep RL for financial trading is demonstrated using experiments on a time series dataset consisting of Foreign Exchange (FOREX) trading pairs prices.