273 citations · 431 across the 15 of their papers we have counts for
3 papers · 1 filter
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints
Anikait Singh, Aviral Kumar, Quan Vuong +2
Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of…
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Aviral Kumar, Anikait Singh, Frederik Ebert +4
Progress in deep learning highlights the tremendous potential of utilizing diverse robotic datasets for attaining effective generalization and makes it enticing to consider leverag…
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
Aviral Kumar, Joey Hong, Anikait Singh +1
Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood…