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Coevolutionary Policy Search

I propose to develop a new class of decision-theoretic planning methods that overcome fundamental obstacles to the efficient optimization of autonomous agents. Creating agents that are effective in diverse settings is a key goal... ver más

30/09/2021

UOXF

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-09-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-StG-2014: ERC Starting Grant

I+D

Cerrada hace 10 años

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2 Participantes

UOXF

1.48M€ | Lider

MUEBLES J. MARIN

376.30K€ | Participante

Últimas noticias

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

01-12-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

01-12-2024: AEI En las últimas 48 horas el Organismo AEI ha otorgado 23 concesiones

Duración del proyecto: 75 meses Fecha Inicio: 2015-06-02
Fecha Fin: 2021-09-30

Presupuesto del proyecto 1M€

Descripción del proyecto I propose to develop a new class of decision-theoretic planning methods that overcome fundamental obstacles to the efficient optimization of autonomous agents. Creating agents that are effective in diverse settings is a key goal of artificial intelligence with enormous potential implications: robotic agents would be invaluable in homes, factories, and high-risk settings; software agents could revolutionize e-commerce, information retrieval, and traffic control. The main challenge lies in specifying an agent's policy: the behavioral strategy that determines its actions. Since the complexity of realistic tasks makes manual policy construction hopeless, there is great demand for decision-theoretic planning methods that automatically discover good policies. Despite enormous progress, the grand challenge of efficiently discovering effective policies for complex tasks remains unmet. A fundamental obstacle is the cost of policy evaluation: estimating a policy's quality by averaging performance over multiple trials. This cost grows quickly with increases in task complexity (making trials more expensive) or stochasticity (necessitating more trials). To address this difficulty, I propose a new approach that simultaneously optimizes both policies and the manner in which those policies are evaluated. The key insight is that, in many tasks, many trials are wasted because they do not elicit the controllable rare events critical for distinguishing between policies. Thus, I will develop methods that leverage coevolution to automatically discover the best events, instead of sampling them randomly. If successful, this project will greatly improve the efficiency of decision-theoretic planning and, in turn, help realize the potential of autonomous agents. In addition, by automatically identifying the most useful events, the resulting methods will help isolate critical factors in performance and thus yield new insights into what makes decision-theoretic problems hard.

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