Adaptive learning from demonstration in heterogeneous agents: Concurrent minimization and maximization of surprise in sparse reward environments

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Clark, Emma

Contributors dc:contributor

• Mehr, Negar

• Reinforcement Learning
• Learning From Demonstration
• Curriculum Learning

Statement dc:rights

• Copyright 2023 Emma Clark

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/122173

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F122173