  Deep or reinforcement learning (RL) approaches have been adapted as reactive
agents to quickly learn and respond with new investment strategies for
portfolio management under the highly turbulent financial market environments
in recent years. In many cases, due to the very complex correlations among
various financial sectors, and the fluctuating trends in different financial
markets, a deep or reinforcement learning based agent can be biased in
maximising the total returns of the newly formulated investment portfolio while
neglecting its potential risks under the turmoil of various market conditions
in the global or regional sectors. Accordingly, a multi-agent and self-adaptive
framework namely the MASA is proposed in which a sophisticated multi-agent
reinforcement learning (RL) approach is adopted through two cooperating and
reactive agents to carefully and dynamically balance the trade-off between the
overall portfolio returns and their potential risks. Besides, a very flexible
and proactive agent as the market observer is integrated into the MASA
framework to provide some additional information on the estimated market trends
as valuable feedbacks for multi-agent RL approach to quickly adapt to the
ever-changing market conditions. The obtained empirical results clearly reveal
the potential strengths of our proposed MASA framework based on the multi-agent
RL approach against many well-known RL-based approaches on the challenging data
sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the
past 10 years. More importantly, our proposed MASA framework shed lights on
many possible directions for future investigation.
