no code implementations • 3 Oct 2023 • Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares
In recent years, the integration of Automated Planning (AP) and Reinforcement Learning (RL) has seen a surge of interest.
no code implementations • 23 Aug 2023 • Carlos Núñez-Molina, Masataro Asai, Juan Fernández-Olivares, Pablo Mesejo
This results in a different loss function from the MSE commonly employed in the literature, which implicitly models the learned heuristic as a gaussian distribution.
no code implementations • 20 Apr 2023 • Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares
Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically.
1 code implementation • 24 Jan 2023 • Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares
In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve.
no code implementations • 9 Nov 2021 • José Á. Segura-Muros, Juan Fernández-Olivares, Raúl Pérez
The algorithm presented here improves the learning capabilities of PlanMiner when using noisy data as input.
no code implementations • 22 Dec 2020 • Carlos Núñez-Molina, Vladislav Nikolov, Ignacio Vellido, Juan Fernández-Olivares
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.