11 citations · 21 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +2
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even t…