38 citations · 141 across the 18 of their papers we have counts for
6 papers · 1 filter
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam, Juyong Lee, Jesse Zhang +3
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In o…
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Zuxin Liu, Jesse Zhang, Kavosh Asadi +4
The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challe…
Learning to Synthesize Programs as Interpretable and Generalizable Policies
Dweep Trivedi, Jesse Zhang, Shao-Hua Sun +1
Recently, deep reinforcement learning (DRL) methods have achieved impressive performance on tasks in a variety of domains. However, neural network policies produced with DRL method…
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
Jesse Zhang, Haonan Yu, Wei Xu
We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve spa…
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Avi Singh, Albert Yu, Jonathan Yang +3
Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amou…
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang, Brian Cheung, Chelsea Finn +2
Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous, imperiling the RL agent, other agents, and the environment. To overcome t…