Reinforcement Learning for Financial Portfolio Optimization: Dynamic Strategies for Risk and Reward Management

Abstract

Since the techniques of Reinforcement learning (RL) can actually produce dynamic decisions under uncertainty in the
financial portfolio optimization, therefore, it is a critical area of research. A review in recent advancements in the
application of RL for portfolio management has been done in this paper, with an emphasis on its possibility of
enhancing the associated risk management as well as optimizing returns in complex financial markets. First, we outline
the basic principles of RL and discuss its wide range of applications in portfolio optimization. Thereafter, the paper
moves on to major challenges in this field, that is, big data issues, non-stationary environments, and computational
complexities. Last, we will be showing future directions for the research which may include the integration of meta
learning, multi-agent systems, and real-time adaptability for further enhancing the performance of RL-based portfolio
optimization systems.