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"Dueling"
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Dueling
Looks at the history of dueling, from its origins in medieval times to the more recent pistol duels of the \"wild west.\"
Book
Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks
In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.
Journal Article
Les Duels célèbres
Extrait: \"Le duel de femmes est chose rare en France, aujourd'hui surtout que la race des Richelieu a disparu. En faisant bien des recherches, nous avons fini par en découvrir un dans la Gironde. Deux de ces vierges folles, que le Gil-Blas désigne sous le nom charmant de tendresses, ou d'horizontales, se disputaient le cœur et la bourse d'un jeune propriétaire de Bordeaux, le comte de G…é.\" À PROPOS DES ÉDITIONS LIGARAN: Les éditions LIGARAN proposent des versions numériques de grands classiques de la littérature ainsi que des livres rares, dans les domaines suivants: • Fiction: roman, poésie, théâtre, jeunesse, policier, libertin. • Non fiction: histoire, essais, biographies, pratiques.
eBook
Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm
Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments.
Journal Article
A Hybrid Deep Reinforcement Learning Architecture for Optimizing Concrete Mix Design Through Precision Strength Prediction
Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R2 of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments.
Journal Article
Smile of the wolf
\"Eleventh-century Iceland. One night in the darkness of winter, two friends set out on an adventure but end up killing a man. Kjaran, a travelling poet who trades songs for food and shelter, and Gunnar, a feared warrior, must make a choice: conceal the deed or confess to the crime and pay the blood price to the family. But their decision leads to a brutal feud: one man is outlawed, free to be killed by anyone without consequence; the other remorselessly hunted by the dead man's kin. Set in a world of ice and snow, it is an epic story of exile and revenge, of duels and betrayals, and two friends struggling to survive in a desolate landscape, where honour is the only code that men abide by.
Book
Graph-Density-Aware Joint Energy-Latency Optimization in Multi-UAV IoT Networks Using Dueling Deep Q-Network
Multi-UAV communication networks face significant challenges in achieving high energy efficiency and low communication latency under dynamic topology and interference conditions. This paper proposes a Dueling Deep Q-Network (DQN) framework for joint resource optimization in 6G-enabled multi-UAV systems. The proposed approach jointly optimizes transmit power allocation, inter-UAV link association, and adaptive graph density within a unified reinforcement learning framework. By employing a dueling value–advantage decomposition, the proposed model improves learning stability and convergence compared to conventional DQN and Double DQN (DDQN) schemes. Simulation results under varying network densities and UAV scales show that the proposed Dueling DQN achieves up to 15% higher energy efficiency and 12% lower end-to-end latency, while maintaining robust performance in dense connectivity scenarios. These results demonstrate the effectiveness and scalability of the proposed framework for energy- and latency-sensitive UAV communication applications.
Journal Article