| Autor: |
Justin Svegliato, Shlomo Zilberstein, Samer B. Nashed, Rod Grupen, Connor Basich, Matteo Brucato |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ICRA |
| Popis: |
Autonomous systems often use approximate planners that exploit state abstractions to solve large MDPs in real-time decision-making problems. However, these planners can eliminate details needed to produce effective behavior in autonomous systems. We therefore propose a novel model, a partially abstract MDP, with a set of abstract states that each compress a set of ground states to condense irrelevant details and a set of ground states that expand from a set of expanded abstract states to retain relevant details. This papers offers (1) a definition of a partially abstract MDP that (2) generalizes its ground MDP and its abstract MDP and exhibits bounded optimality depending on its abstract MDP along with (3) a lazy algorithm for planning and execution in autonomous systems. The result is a scalable approach that computes near-optimal solutions to large problems in minutes rather than hours. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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