1 citations · 1 across the 3 of their papers we have counts for
6 papers
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability
Marcel Petran, Stefan Edelkamp
Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent. We study a spatial opponent model: a transformer…
Planning with Vision-Language Models and a Use Case in Robot-Assisted Teaching
Xuzhe Dang, Lada Kudláčková, Stefan Edelkamp
Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, particularly for complex real-world…
SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning
Xuzhe Dang, Stefan Edelkamp
Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-bas…
Imperfect-Information Games on Quantum Computers: A Case Study in Skat
Ulrich Armbrüster, Stefan Edelkamp, Gabriel Maresch +1
For decades it is known that Quantum Computers might serve as a tool to solve a very specific kind of problems that have long thought to be incalculable. Some of those problems are…
CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations
Xuzhe Dang, Stefan Edelkamp
This paper presents a novel method for learning reward functions for robotic motions by harnessing the power of a CLIP-based model. Traditional reward function design often hinges…
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp +1
In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient condi…