Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments
Clark, Emma
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Permalink
https://hdl.handle.net/2142/122173
Description
Title
Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments
Author(s)
Clark, Emma
Issue Date
2023-12-05
Director of Research (if dissertation) or Advisor (if thesis)
Mehr, Negar
Department of Study
Aerospace Engineering
Discipline
Aerospace Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
reinforcement learning
learning from demonstration, curriculum learning
Abstract
Learning from Demonstration (LfD) is a reinforcement learning method where an agent learns a policy by imitation demonstrations from an expert. This expert can be another agent, already trained to have an optimal policy or a predefined control system. Or, the expert can be a human. LfD is useful for learning very complex tasks or in settings with strict behavior guidelines or restrictions. One of the major limitations of LfD is an inability to learn when there are differences in dynamics between the student and teacher agents. This limits LfD methods to homogenous agents; however, real-world scenarios may often have differences in dynamics or environmental constraints between the student and teacher. Such as, a robot learning from human demonstration, or two different models of robot with variations in maximum joint angles or actuator power. Even analogous systems may have small variations in robot capabilities, due to noise or under-performance from technological limitations. To address this challenge, we propose a Student-Teacher framework, where the Teacher agent uses the Student’s surprise with, respect to demonstration trajectories, to infer differences in dynamics between itself and the Student. The teacher is then able to adapt its demonstration trajectories to consider the dynamics or constraints of the Student. In contrast to most common LfD methods, we assume the Teacher is not already an expert, but instead is learning in parallel to the Student.
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