465 citations · 1.5k across the 21 of their papers we have counts for
6 papers · 1 filter
URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu +6
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to…
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi +2
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations…
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan +2
A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have show…
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran +6
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer ar…
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
Catherine Cang, Aravind Rajeswaran, Pieter Abbeel +1
Offline Reinforcement Learning (RL) aims to extract near-optimal policies from imperfect offline data without additional environment interactions. Extracting policies from diverse…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…