11 papers · 1 filter
Sample-efficient and Scalable Exploration in Continuous-Time RL
Klemens Iten, Lenart Treven, Bhavya Sukhija +2
Reinforcement learning algorithms are typically designed for discrete-time dynamics, even though the underlying real-world control systems are often continuous in time. In this pap…
SOMBRL: Scalable and Optimistic Model-Based RL
Bhavya Sukhija, Lenart Treven, Carmelo Sferrazza +3
We address the challenge of efficient exploration in model-based reinforcement learning (MBRL), where the system dynamics are unknown and the RL agent must learn directly from onli…
Simulation Priors for Data-Efficient Deep Learning
Lenart Treven, Bhavya Sukhija, Jonas Rothfuss +3
How do we enable AI systems to efficiently learn in the real-world? First-principles models are widely used to simulate natural systems, but often fail to capture real-world comple…
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Bhavya Sukhija, Stelian Coros, Andreas Krause +2
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms u…
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Yarden As, Bhavya Sukhija, Lenart Treven +3
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with…
Learning Safety Constraints for Large Language Models
Xin Chen, Yarden As, Andreas Krause
Large language models (LLMs) have emerged as powerful tools but pose significant safety risks through harmful outputs and vulnerability to adversarial attacks. We propose SaP, shor…