Deep reinforcement learning (DRL) is a prominent technique for stock market prediction due to the ability to learn an optimum information-based strategy. However, due to time and budget constraints, training the DRL model necessitates a high volume of interactions with the environment, which is impossible in financial markets. Training is therefore typically done in simulated settings. On the other hand, real stock markets differ from simulations in that uncertainty is a fundamental feature of them. Examples of these uncertainties include volatility, transaction costs, and liquidity. The usefulness of policy starts to diminish at this point when it is implemented in the actual world. In this paper, we present an adaptive method for stock price prediction in a volatile environment using the Recurrent Deep Deterministic Policy Gradient. Our model enhances the robustness of policy learning with better flexibility by directly integrating uncertainties, such as price movement and time-varying market conditions, into policy learning. This ensures better decision-making and performance forecasting when applied to the actual stock market environment. In addition, we provide a considerably more efficient way to learn temporal patterns in financial time-series data by integrating RDDPG into FinRL-Meta. It can improve the framework’s ability to function in dynamic or fluctuating market conditions, enabling it to make stronger financial decisions.