2 citations · 6 across the 10 of their papers we have counts for
11 papers
Self-Repairing Recurrent Ensembles for Real-Time Recovery from Distribution Shift
Julian Lemmel, Pedro D. Wendel Garcia, Taisuke Kobayashi +1
Deploying a pretrained controller exposes it to conditions that are absent from its training data. Sensor drift, outright sensor failure and accumulating measurement noise all indu…
Emergent swimming strategies of a smart three-bead swimmer
Ruma Maity, Maximilian Huebl, Julian Lemmel +2
Low-Reynolds-number microswimmers have recently attracted much interest for their ubiquity in biology and their applications in biotechnology and medicine. However, a key obstacle…
Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Julian Lemmel, Felix Resch, Mónika Farsang +3
We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a memory-efficient algorithm that update…
TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubes
Julian Lemmel, Manuel Kranzl, Adam Lamine +3
Interactive Imitation Learning deals with training a novice policy from expert demonstrations in an online fashion. The established DAgger algorithm trains a robust novice policy b…
Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
Julian Lemmel, Manuel Kranzl, Adam Lamine +3
This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are kn…
A quantum-classical reinforcement learning model to play Atari games
Dominik Freinberger, Julian Lemmel, Radu Grosu +1
Recent advances in reinforcement learning have demonstrated the potential of quantum learning models based on parametrized quantum circuits as an alternative to deep learning model…
Scalable Offline Reinforcement Learning for Mean Field Games
Axel Brunnbauer, Julian Lemmel, Zahra Babaiee +2
Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend…
Real-Time Recurrent Reinforcement Learning
Julian Lemmel, Radu Grosu
We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). The proposed algorithm combines three integral part…
Prediction of Tourism Flow with Sparse Geolocation Data
Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner +5
Modern tourism in the 21st century is facing numerous challenges. Among these the rapidly growing number of tourists visiting space-limited regions like historical cities, museums…
On the Benefits of Biophysical Synapses
Julian Lemmel, Radu Grosu
The approximation capability of ANNs and their RNN instantiations, is strongly correlated with the number of parameters packed into these networks. However, the complexity barrier…
Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data
Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner +5
Modern tourism in the 21st century is facing numerous challenges. One of these challenges is the rapidly growing number of tourists in space limited regions such as historical city…