Importance of the features used as the state space of the DRL agent for financial portfolio management experiments.

<p>We can see how the APPLE close value is the most important for the estimated policy of the DRL agent.</p>

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Bibliographic Details
Main Author:	Alejandra de-la-Rica-Escudero (20570535) (author)
Other Authors:	Eduardo C. Garrido-Merchán (18830597) (author), María Coronado-Vaca (20570538) (author)
Published:	2025
Subjects:	Science Policy Virology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified successfully addressed recently high volatility markets every action performed deep reinforcement learning also called gymnasium universal approximator models markowitz model rely proximal policy optimization novel explainable drl making drl explainable drl algorithms train agent &# 8217 methods rely alternative models investment policy drl algorithm drl agents technological sector quantitative researchers portfolio management financial state feature importance expected reward enhance transparency empirically illustrate
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