CNN-DRL with Shuffled Features in Finance
This work addresses reward optimization in financial AI applications, but it appears incremental as it builds on prior CNN-DRL methods with a specific feature shuffling technique.
The study tackled the problem of improving reward in financial deep reinforcement learning by applying a specific permutation to feature vectors to create a CNN matrix that strategically positions more pertinent features closely, resulting in a substantial enhancement in reward attainment.
In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment.