Deep Q-Network
PulseAugur coverage of Deep Q-Network — every cluster mentioning Deep Q-Network across labs, papers, and developer communities, ranked by signal.
1 天有情绪数据
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Meta-learning framework accelerates control system adaptation with limited data
Researchers have developed a novel meta-learning framework for designing optimal controllers for uncertain nonlinear systems, particularly when target system data is scarce. This approach leverages offline data from sim…
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New research challenges independence assumption in Deep Q-Learning algorithms
Researchers have developed a new statistical analysis for Deep Q-Networks (DQN) that accounts for temporal dependence in training data. This approach models minibatches as $\tau$-mixing, moving beyond the typical assump…
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NaviGNN AI framework optimizes sustainable mobility in futuristic smart cities
Researchers have developed NaviGNN, a novel AI system designed to optimize mobility in futuristic smart cities with complex vertical and linear structures. This system integrates multi-agent reinforcement learning and g…
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Reinforcement learning enhances autonomous target tracking accuracy and robustness
Researchers have developed a deep reinforcement learning approach for autonomous bearings-only tracking of moving targets. The system formulates the observer maneuver problem as a belief Markov decision process, using a…
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AI routing framework boosts LEO satellite network performance and efficiency
Researchers have developed a novel spatial-temporal learning-based distributed routing framework designed for dynamic Low Earth Orbit (LEO) satellite networks. This framework integrates Graph Attention Networks (GAT) an…
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DRL framework optimizes NR-U/Wi-Fi coexistence for fairness and throughput
Researchers have developed a policy-driven deep reinforcement learning framework to manage resource allocation between NR-U and Wi-Fi networks operating in unlicensed spectrum. This framework uses a deep Q-network to le…
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New C++ engine HASE achieves 33M steps/sec for multi-agent RL training
Researchers have developed a new C++ engine called Hide-And-Seek-Engine (HASE) designed to significantly improve the efficiency of training reinforcement learning agents in decentralized, partially observable environmen…
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LLMs fine-tuned for traffic control with critic-guided reinforcement learning
Researchers have developed DGLight, a novel framework that fine-tunes large language models for traffic signal control. This approach utilizes a Deep Q-Network critic to guide the optimization process, enabling the mode…
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OpenAI and researchers reveal AI vulnerabilities to adversarial attacks
OpenAI researchers are exploring the transferability of adversarial robustness across different types of perturbations in neural networks. Their findings indicate that robustness against one perturbation type does not a…