4 citations · 13 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
NeoRL: Efficient Exploration for Nonepisodic RL
Bhavya Sukhija, Lenart Treven, Florian Dörfler +2
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single t…
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
Lenart Treven, Bhavya Sukhija, Yarden As +2
Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP). However, various systems are inherently continuous in time, making disc…
Bridging the Sim-to-Real Gap with Bayesian Inference
Jonas Rothfuss, Bhavya Sukhija, Lenart Treven +3
We present SIM-FSVGD for learning robot dynamics from data. As opposed to traditional methods, SIM-FSVGD leverages low-fidelity physical priors, e.g., in the form of simulators, to…
Transductive Active Learning: Theory and Applications
Jonas Hübotter, Bhavya Sukhija, Lenart Treven +2
We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, w…
Active Few-Shot Fine-Tuning
Jonas Hübotter, Bhavya Sukhija, Lenart Treven +2
We study the question: How can we select the right data for fine-tuning to a specific task? We call this data selection problem active fine-tuning and show that it is an instance o…