Generalized Policy Learning with Planning

Generalized policy learning seeks to find policies that solve multiple tasks within a planning domain. We introduce methods to search for policies independently in a domain from empty initialized policies. As an extension, we also propose a problem setting to learn satisficing policies between domains. In an independent domain, we propose a score function to guide the policy search. Our approach, Policy-Guided Planning for Generalized Policy Generation (PG3), evaluates policies based on how well it can be used to plan. Empirically, we show that PG3 allows generalized policy learning to occur more efficiently than other baselines with PDDL-based problems and policies represented as lifted decision lists. Finally, our experiments show that policies independently learned are qualitiatively similar, prompting further investigation on the possibilities of further accelerating the policy search process.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Yang, Ryan P.

Advisor dc:contributor.advisor

• Kaelbling, Leslie P.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162962

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162962