Q-learning
PulseAugur coverage of Q-learning — every cluster mentioning Q-learning across labs, papers, and developer communities, ranked by signal.
3 天有情绪数据
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New Q-learning algorithm robust to corrupted rewards
Researchers have developed a new variant of Q-learning designed to handle adversarially corrupted rewards in reinforcement learning settings. This novel algorithm is analyzed under asynchronous sampling conditions and p…
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New Q-learning method achieves n^{-1/4} Gaussian approximation bound
Researchers have developed a new method for approximating Gaussian distributions in entropy-regularized Q-learning with function approximation. The study establishes convergence rates for averaged iterates generated by …
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Q-Learning Error Analysis Reveals Overestimation Dynamics
Researchers have developed a novel finite-time error analysis for Q-learning algorithms using constant step sizes. The analysis decomposes the error into negative and positive components, revealing that the negative par…
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Q-learning agent mimics insect behavior for odor source detection
Researchers have developed a Q-learning agent capable of navigating turbulent flows to find odor sources, utilizing a minimal memory of the time elapsed since the last scent detection. This agent successfully learned st…
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LLM and Q-learning enhance cloud intrusion detection system
Researchers have developed a novel multi-layer intrusion detection system (IDS) for cloud environments that integrates large language models (LLMs) and adaptive Q-learning. This system operates across network, host, and…
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New Long-Horizon Q-Learning method improves reinforcement learning accuracy
Researchers have introduced Long-Horizon Q-Learning (LQL), a novel method designed to improve the stability of value-based reinforcement learning. LQL addresses the issue of compounding estimation errors in traditional …
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New ME-AM framework enhances offline RL with entropy maximization
Researchers have introduced Maximum Entropy Adjoint Matching (ME-AM), a new framework designed to improve offline reinforcement learning. This method addresses limitations in existing approaches, such as popularity bias…
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New Q-learning theory offers tighter convergence rate analysis
Researchers have developed a novel theoretical framework for analyzing Q-learning, a fundamental algorithm in reinforcement learning. This new approach views Q-learning through the lens of switching systems, deriving a …
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Researchers develop MDP and POMDP for error mitigation in digital twins
Researchers have developed a new framework for mitigating error propagation in modular digital twins by treating it as a sequential decision-making problem. They formulated this using a Markov Decision Process (MDP) and…
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Replit and Weights & Biases host ML hackathon, award prizes
Replit and Weights & Biases recently concluded their first machine learning hackathon, which ran from February 4-11, 2023. Participants worldwide used Replit's platform and Weights & Biases' tools to build and fine-tune…