12 citations · 18 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Learning Soft Robotic Dynamics with Active Exploration
Hehui Zheng, Bhavya Sukhija, Chenhao Li +3
Soft robots offer unmatched adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control notoriousl…
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…
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…