9 papers
Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning representations for solutions of constrained optimization problems (COPs) with unknown cost functions is challenging, as models like (Variational) Autoencoders struggle to…
DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation
Ahmad Saeed Khan, Erik Schaffernicht, Johannes Andreas Stork
Estimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups. Propensity score-…
ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht +2
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. Thi…
On the Fly Adaptation of Behavior Tree-Based Policies through Reinforcement Learning
Marco Iannotta, Johannes A. Stork, Erik Schaffernicht +1
With the rising demand for flexible manufacturing, robots are increasingly expected to operate in dynamic environments where local -- such as slight offsets or size differences in…
On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
Ahmad Saeed Khan, Erik Schaffernicht, Johannes Andreas Stork
Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias…
Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Mathematical solvers use parametrized Optimization Problems (OPs) as inputs to yield optimal decisions. In many real-world settings, some of these parameters are unknown or uncerta…
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning. Yet, existing methods…
Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies
Finn Rietz, Erik Schaffernicht, Stefan Heinrich +1
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both…
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
Finn Rietz, Erik Schaffernicht, Stefan Heinrich +1
Reinforcement learning (RL) for complex tasks remains a challenge, primarily due to the difficulties of engineering scalar reward functions and the inherent inefficiency of trainin…