Optimizing Reinforcement Learning in High-Uncertainty Environments

Abstract

Deep reinforcement learning (DRL) has recently been found to be quite beneficial for financial decision-making because it can effectively learn an optimal strategy in a highly volatile market environment. The problem with conventional RL model arises in their inherent problems related to the stability and efficiency of exploration in highly uncertain environments. However, high levels of volatility, stochasticity of the prices, and information incompleteness in financial data tend to generate highly unstable convergence and inefficient exploration in the process of learning. This research paper aims at presenting a novel Intelligent Harmony Search–Adaptive Twin Delayed Deep Deterministic Policy Gradient (IntHS-ATD3PG) framework that will enable financial decision-makers to make profitable decisions under conditions of high volatility and uncertainty. To do that, financial data will be collected from publicly available sources and preprocessed through min-max normalization. Features will be extracted with the help of the Discrete Wavelet Transform (DWT) method. These features will be utilized by the proposed ATD3PG model in order to learn an optimal strategy in the continuous action space. Experimental results demonstrate superior performance, achieving a cumulative return of 19.5%, a Sharpe ratio of 3.70, and reduced volatility of 10.4%, outperforming conventional RL models. The proposed method, implemented using Python, provides a scalable and robust solution for adaptive financial optimization.