|
Title:
|
Adaptive Envelope MDPs for Relational Equivalence-based Planning |
|
Author:
|
Gardiol, Natalia H.; Kaelbling, Leslie Pack |
|
Other Contributors:
|
Learning and Intelligent Systems |
|
Advisor:
|
Leslie Kaelbling |
|
Issue Date:
|
2008-07-29 |
|
Abstract:
|
We describe a method to use structured representations of the environment’s dynamics to constrain and speed up the planning process. Given a problem domain described in a probabilistic logical description language, we develop an anytime technique that incrementally improves on an initial, partial policy. This partial solution is found by ï¬rst reducing the number of predicates needed to represent a relaxed version of the problem to a minimum, and then dynamically partitioning the action space into a set of equivalence classes with respect to this minimal representation. Our approach uses the envelope MDP framework, which creates a Markov decision process out of a subset of the full state space as de- termined by the initial partial solution. This strategy permits an agent to begin acting within a restricted part of the full state space and to expand its envelope judiciously as resources permit. |
|
URI:
|
http://hdl.handle.net/1721.1/41920
|
|
Other Identifiers:
|
MIT-CSAIL-TR-2008-050 |
|
Related To
|
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
|
