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20172022
most citedGenerative predecessor models for sample-efficient imitation learning

11 citations · 21 across the 5 of their papers we have counts for

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cs.LG2022

Meta-Gradients in Non-Stationary Environments

Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker +3

Meta-gradient methods (Xu et al., 2018; Zahavy et al., 2020) offer a promising solution to the problem of hyperparameter selection and adaptation in non-stationary reinforcement le…

cs.LG20207 cited

Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning

Yannick Schroecker, Charles Isbell

This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning. In either case, effective solutions require the agent to reliably reach a…

cs.LG20191 cited

Active Learning within Constrained Environments through Imitation of an Expert Questioner

Kalesha Bullard, Yannick Schroecker, Sonia Chernova

Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic huma…

cs.LG201911 cited

Generative predecessor models for sample-efficient imitation learning

Yannick Schroecker, Mel Vecerik, Jonathan Scholz

We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observe…

cs.LG2018

Imitating Latent Policies from Observation

Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker +1

In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal…