Sökning: id:"swepub:oai:DiVA.org:su-209701" >
EpidRLearn :
EpidRLearn : Learning Intervention Strategies for Epidemics with Reinforcement Learning
-
- Bampa, Maria (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
- Fasth, Tobias, 1980- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
- Magnússon, Sindri, 1987- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
visa fler...
-
- Papapetrou, Panagiotis, 1981- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
-
visa färre...
-
(creator_code:org_t)
- 2022-07-09
- 2022
- Engelska.
-
Ingår i: Artificial Intelligence in Medicine. - Cham : Springer Nature. - 9783031093425 ; , s. 189-199
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Reinforcement learning
- Mitigation policies
- COVID-19
- data- och systemvetenskap
- Computer and Systems Sciences
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas