Saved in:
| Main Authors: | Maus, Tom, Atamna, Asma, Glasmachers, Tobias |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.00762 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
by: Maus, Tom, et al.
Published: (2025)
by: Maus, Tom, et al.
Published: (2025)
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
by: Atamna, Asma, et al.
Published: (2025)
by: Atamna, Asma, et al.
Published: (2025)
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
by: Pendyala, Abhijeet, et al.
Published: (2024)
by: Pendyala, Abhijeet, et al.
Published: (2024)
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
by: Maus, Tom, et al.
Published: (2026)
by: Maus, Tom, et al.
Published: (2026)
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
by: Maus, Tom, et al.
Published: (2025)
by: Maus, Tom, et al.
Published: (2025)
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
by: Pendyala, Abhijeet, et al.
Published: (2025)
by: Pendyala, Abhijeet, et al.
Published: (2025)
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
by: Hakenes, Simon, et al.
Published: (2025)
by: Hakenes, Simon, et al.
Published: (2025)
Comparison of Outlier Detection Algorithms on String Data
by: Maus, Philip
Published: (2026)
by: Maus, Philip
Published: (2026)
Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments
by: Yang, Xue, et al.
Published: (2025)
by: Yang, Xue, et al.
Published: (2025)
Leveraging Reinforcement Learning, Genetic Algorithms and Transformers for background determination in particle physics
by: Mendizabal, Guillermo Hijano, et al.
Published: (2025)
by: Mendizabal, Guillermo Hijano, et al.
Published: (2025)
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
by: Sziburis, Tim, et al.
Published: (2024)
by: Sziburis, Tim, et al.
Published: (2024)
ProtoP-OD: Explainable Object Detection with Prototypical Parts
by: Rath-Manakidis, Pavlos, et al.
Published: (2024)
by: Rath-Manakidis, Pavlos, et al.
Published: (2024)
Leveraging Knowledge Distillation for Efficient Deep Reinforcement Learning in Resource-Constrained Environments
by: Meng, Guanlin
Published: (2023)
by: Meng, Guanlin
Published: (2023)
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
by: Han, Zhixin, et al.
Published: (2026)
by: Han, Zhixin, et al.
Published: (2026)
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
by: Nakashima, So, et al.
Published: (2024)
by: Nakashima, So, et al.
Published: (2024)
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
by: Müller, Arthur, et al.
Published: (2021)
by: Müller, Arthur, et al.
Published: (2021)
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
by: Wagenmaker, Andrew, et al.
Published: (2024)
by: Wagenmaker, Andrew, et al.
Published: (2024)
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
by: Zeng, Siliang, et al.
Published: (2023)
by: Zeng, Siliang, et al.
Published: (2023)
Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations
by: Tang, Zuojin, et al.
Published: (2024)
by: Tang, Zuojin, et al.
Published: (2024)
Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning
by: Zilberstein, Itai, et al.
Published: (2025)
by: Zilberstein, Itai, et al.
Published: (2025)
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
by: Rimon, Zohar, et al.
Published: (2024)
by: Rimon, Zohar, et al.
Published: (2024)
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
by: Gu, Shengda, et al.
Published: (2025)
by: Gu, Shengda, et al.
Published: (2025)
How to Leverage Diverse Demonstrations in Offline Imitation Learning
by: Yue, Sheng, et al.
Published: (2024)
by: Yue, Sheng, et al.
Published: (2024)
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling
by: Müller, Arthur, et al.
Published: (2024)
by: Müller, Arthur, et al.
Published: (2024)
Safe Reinforcement Learning for Real-World Engine Control
by: Bedei, Julian, et al.
Published: (2025)
by: Bedei, Julian, et al.
Published: (2025)
Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning
by: Krau, Tatjana, et al.
Published: (2026)
by: Krau, Tatjana, et al.
Published: (2026)
Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning
by: Shen, Chengyandan, et al.
Published: (2025)
by: Shen, Chengyandan, et al.
Published: (2025)
An Efficient Open World Environment for Multi-Agent Social Learning
by: Ye, Eric, et al.
Published: (2025)
by: Ye, Eric, et al.
Published: (2025)
Efficient Algorithms for Mitigating Uncertainty and Risk in Reinforcement Learning
by: Su, Xihong
Published: (2025)
by: Su, Xihong
Published: (2025)
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch
by: Feng, Shengyu, et al.
Published: (2024)
by: Feng, Shengyu, et al.
Published: (2024)
ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
by: Tan, Wentao, et al.
Published: (2025)
by: Tan, Wentao, et al.
Published: (2025)
From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
by: Zeqiao, Li, et al.
Published: (2025)
by: Zeqiao, Li, et al.
Published: (2025)
Real-World Reinforcement Learning of Active Perception Behaviors
by: Hu, Edward S., et al.
Published: (2025)
by: Hu, Edward S., et al.
Published: (2025)
On Generalization Across Environments In Multi-Objective Reinforcement Learning
by: Teoh, Jayden, et al.
Published: (2025)
by: Teoh, Jayden, et al.
Published: (2025)
Leveraging Lightweight Generators for Memory Efficient Continual Learning
by: Lamers, Christiaan, et al.
Published: (2025)
by: Lamers, Christiaan, et al.
Published: (2025)
Scaling Sim-to-Real Reinforcement Learning for Robot VLAs with Generative 3D Worlds
by: Choi, Andrew, et al.
Published: (2026)
by: Choi, Andrew, et al.
Published: (2026)
Demonstration Guided Multi-Objective Reinforcement Learning
by: Lu, Junlin, et al.
Published: (2024)
by: Lu, Junlin, et al.
Published: (2024)
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
by: Beliaev, Mark, et al.
Published: (2024)
by: Beliaev, Mark, et al.
Published: (2024)
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning
by: Schäfer, Georg, et al.
Published: (2025)
by: Schäfer, Georg, et al.
Published: (2025)
Sample Efficient Demonstration Selection for In-Context Learning
by: Purohit, Kiran, et al.
Published: (2025)
by: Purohit, Kiran, et al.
Published: (2025)
Similar Items
-
Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
by: Maus, Tom, et al.
Published: (2025) -
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
by: Atamna, Asma, et al.
Published: (2025) -
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
by: Pendyala, Abhijeet, et al.
Published: (2024) -
Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control
by: Maus, Tom, et al.
Published: (2026) -
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process
by: Maus, Tom, et al.
Published: (2025)