Planning in hybrid relational MDPs

Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt

Research output: Contribution to journalArticlepeer-review

Abstract

We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects. This combination of hybrid relational domains has so far not received a lot of attention. While both relational and hybrid approaches have been studied separately, planning in such domains is still challenging and often requires restrictive assumptions and approximations. We propose HYPE: a sample-based planner for hybrid relational domains that combines model-based approaches with state abstraction. HYPE samples episodes and uses the previous episodes as well as the model to approximate the Q-function. In addition, abstraction is performed for each sampled episode, this removes the complexity of symbolic approaches for hybrid relational domains. In our empirical evaluations, we show that HYPE is a general and widely applicable planner in domains ranging from strictly discrete to strictly continuous to hybrid ones, handles intricacies such as unknown objects and relational models. Moreover, empirical results showed that abstraction provides significant improvements.
Original languageEnglish
Pages (from-to)1905-1932
Number of pages28
JournalMachine Learning
Volume106
Issue number12
Early online date19 Sep 2017
DOIs
Publication statusPublished - 1 Dec 2017
Event28th International Conference on Automated Planning and Scheduling - Delft, Netherlands
Duration: 24 Jun 201829 Jun 2018
http://icaps18.icaps-conference.org/index.html

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