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Introduction to stochastic gradient methods
Simon Weissmann
These lecture notes provide an introduction to first-order optimization methods with a particular emphasis on stochastic gradient methods. We begin with deterministic gradient base…
Error Bounds for Importance Sampling with Estimated Proposal Distributions
Cathrine Aeckerle-Willems, Ilja Klebanov, Simon Weissmann
Importance sampling with data-driven proposal distributions is widely used in practice. A common workflow first generates an auxiliary sample of size from an approximation of t…
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…