Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

摘要: The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range capabilities from (RL) and (DL) for handling sophisticated dynamic business environments offers vast opportunities. is characterized by scalability with the potential to be applied high-dimensional problems conjunction noisy nonlinear patterns economic data. In this work, we first consider brief review DL, RL, RL diverse applications providing an in-depth insight into state art. Furthermore, architecture investigated order highlight complexity, robustness, accuracy, performance, computational tasks, risk constraints, profitability. survey results indicate that can provide better performance higher accuracy as compared traditional algorithms while facing real at presence parameters ever-increasing uncertainties.