Evaluating meta-reinforcement learning through a HVAC control benchmark

Yashvir Grewal, Frits de Nijs, Sarah Goodwin

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

Meta-Reinforcement Learning (RL) algorithms promise to leverage prior task experience to quickly learn new unseen tasks. Unfortunately, evaluating meta-RL algorithms is complicated by a lack of suitable benchmarks. In this paper we propose adapting a challenging real-world heating, ventilation and air-conditioning (HVAC) control benchmark for meta-RL. Unlike existing benchmark problems, HVAC control has a broader task distribution, and sources of exogenous stochasticity from price and weather predictions which can be shared across task definitions. This can enable greater differentiation between the performance of current meta-RL approaches, and open the way for future research into algorithms that can adapt to entirely new tasks not sampled from the current task distribution.
Original languageEnglish
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence, AAAI-21
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages15785-15786
Number of pages2
ISBN (Electronic)9781577358664
Publication statusPublished - 2021
EventAAAI Conference on Artificial Intelligence 2021 - Virtual, United States of America
Duration: 2 Feb 20219 Feb 2021
Conference number: 35
https://aaai.org/Conferences/AAAI-21/ (Website)

Conference

ConferenceAAAI Conference on Artificial Intelligence 2021
Abbreviated titleAAAI
CountryUnited States of America
Period2/02/219/02/21
Internet address

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