arXiv research

MERLIN tackles multi-objective task scheduling with hierarchical DRL, outperforming existing methods.

Two DRL policies collaborate to solve NP-hard routing problems.

This paper uses NLDT to find interpretable control rules from complex DRL policies.

Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation. However, standard DRL methods often suffer from poor sample efficiency, partially because they aim to be entirely problem-agnostic. In this …

A novel framework combines LLMs and RL for financial portfolio optimization.

Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions t…

Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (…

This research combines DRL with BL model for better portfolio optimization.

Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight ker…

A new method helps deep learning systems adapt to changing conditions.

Paper presents a hybrid framework combining sentiment analysis and market indicators for financial portfolio optimization.

EX-DRL improves extreme quantile prediction for financial risk management.

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning…

This paper applies DRL to mean reversion trading problems.

Enhances cryptocurrency pair trading with DRL, outperforming classical methods.

This study uses DRL to hedge American put options, outperforming traditional methods.

Paper presents first model extraction attack against DRL models.

Paper proposes DRL for unsupervised IoT localization.

Cyclical learning rates improve DRL performance without manual tuning.

We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time of traditional robust learning methods by several orders of magnitude. We analyze the robustness property of DRL, showing that DRL not only…

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe…

This paper uses deep reinforcement learning to automate electric transmission voltage control.

DRL enhances economic modeling with deep learning methods.

Sym-NCO leverages symmetricities to improve DRL-NCO performance.

Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations. However, some attacks assume full availability of the victim model, and some require a huge amount of computation, making them less feasible…

Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement learning (DRL) models in this paper. DRL models have demonstrated human-le…

We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the data distribution from a specified set of distributions. We illustrate a problem with current DRL formulations, which rely on an overly broad…

Paper explains DRL strategies for portfolio management using linear models.

In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is it…

This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…

FinRL-Meta creates diverse market environments for DRL in finance.

This paper analyzes DRL strategies in finance, revealing unique trading patterns and performance differences.

Paper proposes a new DRL algorithm optimizing Spectral Risk Measures for better risk management.

Deep RL model optimizes pedestrian evacuation in multi-exit scenarios.

Enhanced financial reward with shuffled feature CNN-DRL.

This study optimizes DRL for American option hedging with new training methods.

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence…

Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.

CP-DRL improves curriculum reinforcement learning by leveraging causal relationships.

Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, including credit card information in natural language data and identities from face dataset. However, most of these studies focus on supervise…

Paper establishes DRL for high-dimensional rewards.

A new DRL scheme optimizes solving large graphs' maximum independent set problem.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system. Towards this end, a deep reinforcement lear…

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based…

Enhances survival analysis predictions with a robust learning approach.

This paper tackles JSSP with uncertain task durations using DRL.

FinRL-Podracer accelerates DRL trading strategies in finance with high performance and scalability.

This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly learn an approximation of the state-value function ($V$), alongside an approximation of the state-action value function ($Q$). Our analysis…