A Multi-Agent Reinforcement Learning Approach for Stock Portfolio Allocation

Stock portfolio allocation is one of the most challenging and interesting problems of modern finance. Recently, deep reinforcement learning applications have shown promising results in automating portfolio allocation. However, most current approaches use a single agent learning model which could inadequately capture the complex dynamics arising from the interactions of many traders in today's stock market. In this paper, we explore the applicability of multi-agent deep reinforcement learning to this problem by implementing single-agent, 2-agent, 3-agent, and 4-agent deep deterministic policy gradients (DDPG) algorithms in a competitive setting. Upon analyzing the results obtained using standardized metrics, we observe that there is a significant improvement in the performance of our learning models with the introduction of multiple agents.