103 citations · 194 across the 16 of their papers we have counts for
6 papers · 1 filter
Video Prediction Models as Rewards for Reinforcement Learning
Alejandro Escontrela, Ademi Adeniji, Wilson Yan +6
Specifying reward signals that allow agents to learn complex behaviors is a long-standing challenge in reinforcement learning. A promising approach is to extract preferences for be…
Skill-based Model-based Reinforcement Learning
Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee
Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. H…
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
Youngwoon Lee, Joseph J. Lim, Anima Anandkumar +1
Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a polic…
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme +3
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual…
Demonstration-Guided Reinforcement Learning with Learned Skills
Karl Pertsch, Youngwoon Lee, Yue Wu +1
Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations.…
Accelerating Reinforcement Learning with Learned Skill Priors
Karl Pertsch, Youngwoon Lee, Joseph J. Lim
Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for…