Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Fernández Olivares, Juan"'
Publikováno v:
N\'u\~nez-Molina, C., Fern\'andez-Olivares, J., & P\'erez, R. (2022). Learning to select goals in Automated Planning with Deep-Q Learning. Expert Systems with Applications, 202, 117265
In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have tra
Externí odkaz:
http://arxiv.org/abs/2406.14779
In recent years, the integration of Automated Planning (AP) and Reinforcement Learning (RL) has seen a surge of interest. To perform this integration, a general framework for Sequential Decision Making (SDM) would prove immensely useful, as it would
Externí odkaz:
http://arxiv.org/abs/2310.02167
In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has been little theoretical understanding of what they should learn, how
Externí odkaz:
http://arxiv.org/abs/2308.11905
Publikováno v:
Carlos N\'u\~nez Molina, Pablo Mesejo, & Juan Fern\'andez-Olivares. (2024). A review of symbolic, subsymbolic and hybrid methods for sequential decision making. ACM Computing Surveys, 56(11), Article 272, 1-36
In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this article reviews AP, RL and hybrid methods (e.g., nove
Externí odkaz:
http://arxiv.org/abs/2304.10590
In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning or as benchmarks in planning competitions. In most cases, these problems are cr
Externí odkaz:
http://arxiv.org/abs/2301.10280
This paper presents the PlanMiner-N algorithm, a domain learning technique based on the PlanMiner domain learning algorithm. The algorithm presented here improves the learning capabilities of PlanMiner when using noisy data as input. The PlanMiner al
Externí odkaz:
http://arxiv.org/abs/2111.04997
In this work we propose a goal reasoning method which learns to select subgoals with Deep Q-Learning in order to decrease the load of a planner when faced with scenarios with tight time restrictions, such as online execution systems. We have designed
Externí odkaz:
http://arxiv.org/abs/2012.12335
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Expert Systems With Applications 15 September 2022 202
Autor:
Jara, Leonardo, Ariza-Valderrama, Rubén, Fernández-Olivares, Juan, González, Antonio, Pérez, Raúl
Publikováno v:
In Applied Soft Computing Journal January 2022 115