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insight - Reinforcement Learning
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
The author proposes an improved algorithm for adversarial linear mixture MDPs, focusing on unknown transitions and bandit feedback, achieving a regret bound that surpasses previous results.
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Prioritized League Reinforcement Learning addresses challenges in large-scale heterogeneous multiagent systems by promoting cooperation and resolving sample inequality.
Effiziente modellfreie Exploration in Low-Rank MDPs
Effiziente Exploration in Low-Rank MDPs durch das VoX-Algorithmus.
Adversarial Linear Mixture MDPs Algorithm Improvement
Linear mixture MDPs algorithm improvement for adversarial settings.
Verbessertes Algorithmus für adversative lineare Misch-MDPs mit Bandit-Feedback und unbekanntem Übergang
Verbesserter Algorithmus für adversative lineare Misch-MDPs mit Bandit-Feedback und unbekanntem Übergang.
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Slowly changing adversarial bandit algorithms can efficiently handle discounted Markov decision processes.
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