Pubblicazione scientifica

Deep hierarchical reinforcement learning in a markov game applied to fishery management decision making

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità
sicurezza e qualità sui luoghi di lavoro

RIASSUNTO

In this article, we propose an approach based on deep hierarchical reinforcement learning to help the management of a fishery formalized by a markov game. Two actors, the fishermen seeking to maximize their profit and the decisionmakers seeking to manage the exploitation in the best possible way through the introduction of quotas. For each, we test two learning approaches, one based only on observations, the other on pre-processing leading to beliefs. Several learning algorithms are tested and the results are compared. This has allowed us to identify a minimum amount of knowledge to have for optimal quota setting that also leads to significant and sustainable gains without prior knowledge.

DATA

Data di pubblicazione: 01/12/2020

AUTORI

POIRON-GUIDONI NICOLAS
BISGAMBIGLIA PAUL-ANTOINE

ENTE DI AFFERENZA

UNIV CORSICAUMR SCIENCES FOR ENVIRONMENT 6134CORTEFRANCE

RIVISTA

2020 IEEE Symposium Series on Computational Intelligence (SSCI) (Page(s): 1371-1378)

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