# FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities

arXiv: https://arxiv.org/abs/2509.10531 · HTML: https://mlsanity.com/paper/2509.10531/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)

## Built on by (most cited first, 2 of 2)

- [Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios](https://mlsanity.com/paper/2510.07099/) (2025)
- [Portfolio Reinforcement Learning with Scenario-Context Rollout](https://mlsanity.com/paper/2602.24037/) (2026)

## Abstract (arXiv)

Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset allocation using trial-and-error interactions. However, most DRL-based methods are restricted to allocating assets within a pre-defined investment universe and overlook exploring new opportunities. This study introduces an investment landscape that integrates exploiting existing assets with exploring new investment opportunities in an extended universe. The proposed approach leverages two DRL agents and dynamically balances these objectives to adapt to evolving markets while enhancing portfolio performance. One agent allocates assets within the existing universe, while another assists in exploring new opportunities in the extended universe. The effciency of the proposed methodology is determined using two real-world market data sets. The experiments demonstrate the superiority of the suggested approach against the state-of-the-art portfolio strategies and baseline methods.