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pro vyhledávání: '"Guy, Tatiana V."'
The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologicall
Externí odkaz:
http://arxiv.org/abs/2409.06566
Autor:
Ruman, Marko, Guy, Tatiana V.
Deep reinforcement learning has demonstrated superhuman performance in complex decision-making tasks, but it struggles with generalization and knowledge reuse - key aspects of true intelligence. This article introduces a novel approach that modifies
Externí odkaz:
http://arxiv.org/abs/2209.06604
Autor:
Zugarová, Eliška, Guy, Tatiana V.
A problem of learning decision policy from past experience is considered. Using the Fully Probabilistic Design (FPD) formalism, we propose a new general approach for finding a stochastic policy from the past data.
Externí odkaz:
http://arxiv.org/abs/2006.08768
Publikováno v:
In International Journal of Approximate Reasoning May 2017 84:150-158
Publikováno v:
In Information Sciences 10 November 2016 369:532-547
Autor:
Ruman, Marko, Guy, Tatiana V.
Deep reinforcement learning has shown an ability to achieve super-human performance in solving complex reinforcement learning (RL) tasks only from raw-pixels. However, it fails to reuse knowledge from previously learnt tasks to solve new, unseen ones
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c12cdf188953b4401aeaa699fb9d218f
http://arxiv.org/abs/2209.06604
http://arxiv.org/abs/2209.06604
Akademický článek
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Publikováno v:
In IFAC Proceedings Volumes 2007 40(13):250-255