15 citations · 69 across the 19 of their papers we have counts for
7 papers · 1 filter
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Vivek Myers, Andre He, Kuan Fang +7
Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demons…
Survival Instinct in Offline Reinforcement Learning
Anqi Li, Dipendra Misra, Andrey Kolobov +1
We present a novel observation about the behavior of offline reinforcement learning (RL) algorithms: on many benchmark datasets, offline RL can produce well-performing and safe pol…
Improving Offline RL by Blending Heuristics
Sinong Geng, Aldo Pacchiano, Andrey Kolobov +1
We propose Heuristic Blending (HUBL), a simple performance-improving technique for a broad class of offline RL algorithms based on value bootstrapping. HUBL modifies the Bellman op…
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
Anqi Li, Byron Boots, Ching-An Cheng
We study a new paradigm for sequential decision making, called offline policy learning from observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard…
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining
Garrett Thomas, Ching-An Cheng, Ricky Loynd +4
A rich representation is key to general robotic manipulation, but existing approaches to representation learning require large amounts of multimodal demonstrations. In this work we…
Adversarial Model for Offline Reinforcement Learning
Mohak Bhardwaj, Tengyang Xie, Byron Boots +2
We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies…
Provable Reset-free Reinforcement Learning by No-Regret Reduction
Hoai-An Nguyen, Ching-An Cheng
Reinforcement learning (RL) so far has limited real-world applications. One key challenge is that typical RL algorithms heavily rely on a reset mechanism to sample proper initial s…