5 citations · 18 across the 12 of their papers we have counts for
3 papers · 1 filter
Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach
Sebastian Kassing, Thomas Kruse
Stochastic gradient descent (SGD) has been studied extensively over the past decades due to its simplicity and broad applicability in machine learning. In this work, we analyze the…
The Role of Target Update Frequencies in Q-Learning
Simon Weissmann, Tilman Aach, Benedikt Wille +2
The target network update frequency (TUF) is a central stabilization mechanism in (deep) Q-learning. However, their selection remains poorly understood and is often treated merely…
An Approximate Ascent Approach To Prove Convergence of PPO
Leif Doering, Daniel Schmidt, Moritz Melcher +4
Proximal Policy Optimization (PPO) is among the most widely used deep reinforcement learning algorithms, yet its theoretical foundations remain incomplete. Most importantly, conver…