8 papers
Adversarial Training for Deep Hedging in Nonstationary Markets
Philipp J. Schneider, Lukas Looser, Antoine Garin +2
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We…
Optimality of Affine Policies in Distributionally Robust Linear-Quadratic Control with Temporally Correlated Noise
Jakob Nylöf, Daniel Kuhn, John Lygeros +1
We study finite-horizon distributionally robust linear-quadratic control with disturbances that can be arbitrarily correlated in time. The ambiguity set is modeled as a single 2-Wa…
Soft-Radial Projection for Constrained End-to-End Learning
Philipp J. Schneider, Daniel Kuhn
Integrating hard constraints into deep learning is essential for safety-critical systems. Yet existing constructive layers that project predictions onto constraint boundaries face…
Optimality of Linear Policies in Distributionally Robust Linear Quadratic Control
Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit +1
We study a generalization of the classical discrete-time, Linear-Quadratic-Gaussian (LQG) control problem where the noise distributions affecting the states and observations are un…
Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits
Mengmeng Li, Philipp J. Schneider, Jelisaveta Aleksić +1
We introduce the first best-of-both-worlds algorithm for contextual combinatorial semi-bandits that simultaneously guarantees regret in the adve…
Towards Optimal Offline Reinforcement Learning
Mengmeng Li, Daniel Kuhn, Tobias Sutter
We study offline reinforcement learning problems with a long-run average reward objective. The state-action pairs generated by any fixed behavioral policy thus follow a Markov chai…
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li, Daniel Kuhn, Bahar Taşkesen
Follow-The-Regularized-Leader (FTRL) algorithms often enjoy optimal regret for adversarial as well as stochastic bandit problems and allow for a streamlined analysis. Nonetheless,…
A Large Deviations Perspective on Policy Gradient Algorithms
Wouter Jongeneel, Daniel Kuhn, Mengmeng Li
Motivated by policy gradient methods in the context of reinforcement learning, we identify a large deviation rate function for the iterates generated by stochastic gradient descent…