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cs.LG2025
ADDQ: Adaptive Distributional Double Q-Learning
Leif Döring, Benedikt Wille, Maximilian Birr +2
Bias problems in the estimation of -values are a well-known obstacle that slows down convergence of -learning and actor-critic methods. One of the reasons of the success of m…
cs.LG2024
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann, Sara Klein, Waïss Azizian +1
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analy…