CNN-DRL for Scalable Actions in Finance
Sina Montazeri Akram Mirzaeinia Haseebullah Jumakhan Amir Mirzaeinia
The published MLP-based DRL in finance has difficulties in learning the
dynamics of the environment when the action scale increases. If the buying and
selling increase to one thousand shares, the MLP agent will not be able to
effectively adapt to the environment. To address this, we designed a CNN agent
that concatenates the data from the last ninety days of the daily feature
vector to create the CNN input matrix. Our extensive experiments demonstrate
that the MLP-based agent experiences a loss corresponding to the initial
environment setup, while our designed CNN remains stable, effectively learns
the environment, and leads to an increase in rewards.