49 citations · 88 across the 22 of their papers we have counts for
14 papers · 1 filter
Does TabPFN Understand Causal Structures?
Omar Swelam, Lennart Purucker, Jake Robertson +3
Causal discovery is fundamental for multiple scientific domains, yet extracting causal information from real world data remains a significant challenge. Given the recent success on…
Fill in the Blanks: Accelerating Q-Learning with a Handful of Demonstrations in Sparse Reward Settings
Seyed Mahdi Basiri Azad, Joschka Boedecker
Reinforcement learning (RL) in sparse-reward environments remains a significant challenge due to the lack of informative feedback. We propose a simple yet effective method that use…
Inverse Reinforcement Learning via Convex Optimization
Hao Zhu, Yuan Zhang, Joschka Boedecker
We consider the inverse reinforcement learning (IRL) problem, where an unknown reward function of some Markov decision process is estimated based on observed expert demonstrations.…
SR-Reward: Taking The Path More Traveled
Seyed Mahdi B. Azad, Zahra Padar, Gabriel Kalweit +1
In this paper, we propose a novel method for learning reward functions directly from offline demonstrations. Unlike traditional inverse reinforcement learning (IRL), our approach d…
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
Moritz Schneider, Robert Krug, Narunas Vaskevicius +2
Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient da…
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control
Yuan Zhang, Jasper Hoffmann, Joschka Boedecker
Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach…