Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision |
| Subtitle of host publication | ECCV 2024 Workshops, Proceedings |
| Editors | Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 100-116 |
| Number of pages | 17 |
| ISBN (electronic) | 978-3-031-92591-7 |
| ISBN (print) | 9783031925900 |
| Publication status | Published - 12 May 2025 |
| Event | 18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 15634 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Training Reinforcement Learning agents directly in any real-world environment remains difficult, as such scenarios entail the risk of damaging the training setup or violating other safety constraints. The training process itself further requires extensive human supervision and intervention to reset the environment after each episode. Thus, we propose an innovative Safe Reinforcement Learning framework that combines Safe and Resetless RL to autonomously reset environments, while also reducing the number of safety constraint violations. In this context, we develop a novel risk-averse RL agent suitable for stringent safety constraints by combining Safe RL, Distributional RL, and Randomized Ensembled Double Q-Learning. Experiments conducted in a novel mobile robotics scenario indicate that our Safe Resetless RL framework reduces the number of human interactions required during training compared to state-of-the-art methods, filling a gap in current problem formulations and enhancing the autonomy of RL training processes in real-world settings.
Keywords
- Autonomous Navigation, Distributional Reinforcement Learning, Mobile Service Robotics, Resetless Reinforcement Learning, Safe Reinforcement Learning
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Computer Vision : ECCV 2024 Workshops, Proceedings. ed. / Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi. Springer Science and Business Media Deutschland GmbH, 2025. p. 100-116 (Lecture Notes in Computer Science; Vol. 15634 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Safe Resetless Reinforcement Learning
T2 - 18th European Conference on Computer Vision, ECCV 2024
AU - Gottwald, Tristan
AU - Schier, Maximilian
AU - Rosenhahn, Bodo
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/5/12
Y1 - 2025/5/12
N2 - Training Reinforcement Learning agents directly in any real-world environment remains difficult, as such scenarios entail the risk of damaging the training setup or violating other safety constraints. The training process itself further requires extensive human supervision and intervention to reset the environment after each episode. Thus, we propose an innovative Safe Reinforcement Learning framework that combines Safe and Resetless RL to autonomously reset environments, while also reducing the number of safety constraint violations. In this context, we develop a novel risk-averse RL agent suitable for stringent safety constraints by combining Safe RL, Distributional RL, and Randomized Ensembled Double Q-Learning. Experiments conducted in a novel mobile robotics scenario indicate that our Safe Resetless RL framework reduces the number of human interactions required during training compared to state-of-the-art methods, filling a gap in current problem formulations and enhancing the autonomy of RL training processes in real-world settings.
AB - Training Reinforcement Learning agents directly in any real-world environment remains difficult, as such scenarios entail the risk of damaging the training setup or violating other safety constraints. The training process itself further requires extensive human supervision and intervention to reset the environment after each episode. Thus, we propose an innovative Safe Reinforcement Learning framework that combines Safe and Resetless RL to autonomously reset environments, while also reducing the number of safety constraint violations. In this context, we develop a novel risk-averse RL agent suitable for stringent safety constraints by combining Safe RL, Distributional RL, and Randomized Ensembled Double Q-Learning. Experiments conducted in a novel mobile robotics scenario indicate that our Safe Resetless RL framework reduces the number of human interactions required during training compared to state-of-the-art methods, filling a gap in current problem formulations and enhancing the autonomy of RL training processes in real-world settings.
KW - Autonomous Navigation
KW - Distributional Reinforcement Learning
KW - Mobile Service Robotics
KW - Resetless Reinforcement Learning
KW - Safe Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=105006877519&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-92591-7_7
DO - 10.1007/978-3-031-92591-7_7
M3 - Conference contribution
AN - SCOPUS:105006877519
SN - 9783031925900
T3 - Lecture Notes in Computer Science
SP - 100
EP - 116
BT - Computer Vision
A2 - Del Bue, Alessio
A2 - Canton, Cristian
A2 - Pont-Tuset, Jordi
A2 - Tommasi, Tatiana
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 September 2024 through 4 October 2024
ER -