A Neuro-Symbolic Reinforcement Learning Architecture: Integrating Perception, Reasoning, and Control

In recent years, neuro-symbolic learning methods have demonstrated promise in tasks re- quiring a semantic understanding that can often be missed by traditional deep learning techniques. By integrating symbolic reasoning with deep learning, neuro-symbolic architec- tures aim to be both interpretable and flexible. This thesis aims to apply neuro-symbolic learning to the domain of reinforcement learning. First, a simulation environment for robotic manipulation tasks is presented. In this environment, an analysis of policy-gradient-based reinforcement learning algorithms is given. Then, by leveraging the performance of deep learning with the semantic reasoning and interpretability of symbolically defined program- ming, a novel neuro-symbolic learning method is proposed to generalize tasks and motion planning for robotics applications using natural language. This novel neuro-symbolic can be seen as an adaptation of the Neuro-Symbolic Concept Learner[1] developed by IBM Wat- son, in which images and natural language are first processed by convolutional and residual neural networks, respectively, and then parsed by a symbolically reasoned program. Where the architecture proposed in this paper differs is in its use of the Neuro-Symbolic Concept Learner for preprocessing of a given input task, to then inform a reinforcement learning agent of how to act in a given environment. Finally, the novel adaptation of the Neuro-Symbolic Concept Learner is introduced as a method of demonstrating generalizable behavior through symbolic preprocessing.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Ellis, Hunter Wayne

Chairs dc:contributor.committeechair

• Doan, Thinh Thanh
• Hsiao, Michael S.

Committee member dc:contributor.committeemember

• Williams, Ryan K.

• Reinforcement Learning
• Neuro-Symbolic
• Concept Learner
• Robotics

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:43971

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135030