30 citations · 90 across the 12 of their papers we have counts for
29 papers
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL
Wonjoon Goo, Scott Niekum
The goal of offline reinforcement learning (RL) is to find an optimal policy given prerecorded trajectories. Many current approaches customize existing off-policy RL algorithms, es…
Distributional Depth-Based Estimation of Object Articulation Models
Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov +1
We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a pri…
Zero-shot Task Adaptation using Natural Language
Prasoon Goyal, Raymond J. Mooney, Scott Niekum
Imitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent. However, as the complexity of tasks grows, it could be be…
Adversarial Intrinsic Motivation for Reinforcement Learning
Ishan Durugkar, Mauricio Tec, Scott Niekum +1
Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we inve…
Universal Off-Policy Evaluation
Yash Chandak, Scott Niekum, Bruno Castro da Silva +3
When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy. Those predictions must of…
Self-Supervised Online Reward Shaping in Sparse-Reward Environments
Farzan Memarian, Wonjoon Goo, Rudolf Lioutikov +2
We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments by automatically densifying…