96 citations · 359 across the 71 of their papers we have counts for
7 papers · 1 filter
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
Harshit Sikchi, Rohan Chitnis, Ahmed Touati +3
Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward function…
Learning Optimal Advantage from Preferences and Mistaking it for Reward
W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson +4
We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF). Most re…
Contrastive Preference Learning: Learning from Human Feedback without RL
Joey Hejna, Rafael Rafailov, Harshit Sikchi +4
Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular paradigm for aligning models with human intent. Typically RLHF algorithms operate in two phases: first, u…
Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill Learning
Andrew Levy, Sreehari Rammohan, Alessandro Allievi +2
General purpose agents will require large repertoires of skills. Empowerment -- the maximum mutual information between skills and states -- provides a pathway for learning large co…
Granger Causal Interaction Skill Chains
Caleb Chuck, Kevin Black, Aditya Arjun +2
Reinforcement Learning (RL) has demonstrated promising results in learning policies for complex tasks, but it often suffers from low sample efficiency and limited transferability.…
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang +1
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimizati…
Language-guided Task Adaptation for Imitation Learning
Prasoon Goyal, Raymond J. Mooney, Scott Niekum
We introduce a novel setting, wherein an agent needs to learn a task from a demonstration of a related task with the difference between the tasks communicated in natural language.…