Papers Deep Reinforcement Learning
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Sensing Accuracy Optimization for Multi-UAV SAR Interferometry with Data Offloading
The integration of unmanned aerial vehicles (UAVs) with radar imaging sensors has revolutionized the monitoring of dynamic and local Earth surface processes by enabling high-resolution and cost-effective remote sensing. …
Deep Reinforcement LearningEvolutionary AlgorithmsTurning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
Deep reinforcement learning (DRL) agents excel in solving complex decision-making tasks across various domains. However, they often require a substantial number of training steps and a vast experience replay buffer, lead…
counterfactualDecision MakingDeep Reinforcement LearningMuJoCo+1LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) en…
Deep Reinforcement LearningLanguage ModelingLanguage ModellingLarge Language ModelMeta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexi…
Deep Reinforcement LearningFairnessMeta-LearningMeta Reinforcement LearningDeep Reinforcement Learning with Gradient Eligibility Traces
Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are …
Deep Reinforcement LearningMuJoCoreinforcement-learningReinforcement Learning+1Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility…
Deep Reinforcement LearningEdge-computingSchedulingTrajectory PlanningBeyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) systems are increasingly used in safety-critical applications, yet their security remains severely underexplored. This work investigates backdoor attacks, which implant hidden triggers t…
Backdoor AttackDeep Reinforcement LearningGeneralized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks. While prior work, such as the Attend, Adapt, and Transfer (A2T) framework, a…
Atari GamesChatbotComputational EfficiencyDeep Learning+6rQdia: Regularizing Q-Value Distributions With Image Augmentation
rQdia regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/…
continuous-controlContinuous ControlDeep Reinforcement LearningImage Augmentation+1Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning
Deep reinforcement learning has been extensively studied in decision-making processes and has demonstrated superior performance over conventional approaches in various fields, including radar resource management (RRM). H…
Decision MakingDeep Reinforcement LearningManagementreinforcement-learning+1GymPN: A Library for Decision-Making in Process Management Systems
Process management systems support key decisions about the way work is allocated in organizations. This includes decisions on which task to perform next, when to execute the task, and who to assign the task to. Suitable …
Decision MakingDeep Reinforcement LearningManagementMulti-Objective Reinforcement Learning for Cognitive Radar Resource Management
The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-object…
Deep Reinforcement LearningManagementMulti-Objective Reinforcement Learningreinforcement-learning+1Learning-Based Resource Management in Integrated Sensing and Communication Systems
In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves al…
Deep Reinforcement LearningIntegrated sensing and communicationManagementTRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
Generalizing deep reinforcement learning agents to unseen environments remains a significant challenge. One promising solution is Unsupervised Environment Design (UED), a co-evolutionary framework in which a teacher adap…
Deep Reinforcement LearningZero-shot GeneralizationEfficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning
Beamforming enhances signal strength and quality by focusing energy in specific directions. This capability is particularly crucial in cell-free integrated sensing and communication (ISAC) systems, where multiple distrib…
Deep Reinforcement LearningGenerative Adversarial NetworkIntegrated sensing and communicationISACOptimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforce…
Deep Reinforcement LearningExperimental DesignModel Predictive Controlparameter estimationBIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordingly. The unpredictability of human behav…
Autonomous VehiclesDecision MakingDeep Reinforcement LearningAdaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning
The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immer…
Deep Reinforcement LearningFederated LearningAdaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
Deep Reinforcement Learning (DRL) has achieved remarkable success in complex sequential decision-making tasks, such as playing Atari 2600 games and mastering board games. A critical yet underexplored aspect of DRL is the…
Atari GamesBoard GamesComputational EfficiencyDecision Making+6A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explor…
Deep Reinforcement LearningSensor FusionSimultaneous Localization and MappingState Estimation