Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Choudhary, Himanshu, Orra, Arishi, Thakur, Manoj
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
by: Choudhary, Himanshu, et al.
Published: (2025)

Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
by: Orra, Arishi, et al.
Published: (2025)

Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research
by: Haque, Mirazul, et al.
Published: (2026)

Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
by: Ndikum, Philip, et al.
Published: (2024)

Discovering Temporally-Aware Reinforcement Learning Algorithms
by: Jackson, Matthew Thomas, et al.
Published: (2024)

Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space
by: Remman, Sindre Benjamin, et al.
Published: (2024)

Adaptive Data Exploitation in Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)

Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning
by: Lan, Jiahua, et al.
Published: (2025)

FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
by: Li, Yang, et al.
Published: (2025)

Discovering Reinforcement Learning Interfaces with Large Language Models
by: Jaswal, Akshat Singh, et al.
Published: (2026)

Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
by: Gu, Shangding, et al.
Published: (2024)

Generative AI in the Construction Industry: Opportunities & Challenges
by: Ghimire, Prashnna, et al.
Published: (2023)

Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
by: Jin, Yonggang, et al.
Published: (2023)

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
by: Daniele, Alessandro, et al.
Published: (2022)

Opportunities and Challenges of Frontier Data Governance With Synthetic Data
by: Thakur, Madhavendra, et al.
Published: (2025)

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2024)

Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024)

Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
by: Shaik, Thanveer, et al.
Published: (2023)

Discovering What You Can Control: Interventional Boundary Discovery for Reinforcement Learning
by: Liu, Jiaxin, et al.
Published: (2026)

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
by: Verma, Abhishek, et al.
Published: (2025)

Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
by: Reijnen, Robbert, et al.
Published: (2022)

Adaptive Negative Reinforcement for LLM Reasoning:Dynamically Balancing Correction and Diversity in RLVR
by: Ingle, Yash, et al.
Published: (2026)

A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
by: Qin, Lang, et al.
Published: (2022)

Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent
by: Hoppmann, Björn, et al.
Published: (2026)

A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2021)

Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making
by: Cimpean, Alexandra, et al.
Published: (2025)

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
by: Wu, Jiaxi, et al.
Published: (2025)

DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
by: Tan, Daniel Jason, et al.
Published: (2025)

Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
by: He, Zhiqiang, et al.
Published: (2025)

Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2025)

Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
by: He, Wenchong, et al.
Published: (2023)

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data
by: Zhou, Yuhang, et al.
Published: (2025)

A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning
by: Xie, Zhengpeng, et al.
Published: (2025)

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
by: de Puiseau, Constantin Waubert, et al.
Published: (2023)

Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
by: Yang, Zixuan, et al.
Published: (2024)

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors
by: Thakur, Himanshu, et al.
Published: (2025)

Learning to Discover at Test Time
by: Yuksekgonul, Mert, et al.
Published: (2026)

An Invitation to Deep Reinforcement Learning
by: Jaeger, Bernhard, et al.
Published: (2023)

Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning
by: Armenta-Segura, Jesús, et al.
Published: (2024)

Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
by: Franceschelli, Giorgio, et al.
Published: (2023)