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cs.LG2025
Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions
Aman Mehra, Alexandre Capone, Jeff Schneider
A long-standing problem in online reinforcement learning (RL) is of ensuring sample efficiency, which stems from an inability to explore environments efficiently. Most attempts at…
cs.RO2025
Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies is heavily dependent on the size and quality of the training dataset. Although large-scale robot and human datasets are increas…
cs.LG2025
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning
Jiayu Chen, Le Xu, Aravind Venugopal +1
Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world m…