982 citations · 2.8k across the 47 of their papers we have counts for
11 papers · 1 filter
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Hankook Lee, Kibok Lee, Kimin Lee +2
Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random…
B-Pref: Benchmarking Preference-Based Reinforcement Learning
Kimin Lee, Laura Smith, Anca Dragan +1
Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based…
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…
Towards More Generalizable One-shot Visual Imitation Learning
Zhao Mandi, Fangchen Liu, Kimin Lee +1
A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches t…
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…
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Seunghyun Lee, Younggyo Seo, Kimin Lee +2
Recent advance in deep offline reinforcement learning (RL) has made it possible to train strong robotic agents from offline datasets. However, depending on the quality of the train…
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…
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Kimin Lee, Laura Smith, Pieter Abbeel
Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet eas…
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Abdus Salam Azad, Edward Kim, Qiancheng Wu +4
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scena…
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Lili Chen, Kimin Lee, Aravind Srinivas +1
Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficienc…
State Entropy Maximization with Random Encoders for Efficient Exploration
Younggyo Seo, Lili Chen, Jinwoo Shin +3
Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL). However, efficient exploration in high-dimensional observ…