activity
20112023
most citedA unified view of entropy-regularized Markov decision processes

98 citations · 343 across the 26 of their papers we have counts for

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17 papers · 1 filter

cs.AI2023

Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not extend trivially to Generalized Planning (GP). GP aims to…

cs.AI2022★ 1 cited

Computing Programs for Generalized Planning as Heuristic Search

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This…

cs.AI2022★ 4 cited

Scaling-up Generalized Planning as Heuristic Search with Landmarks

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson +1

Landmarks are one of the most effective search heuristics for classical planning, but largely ignored in generalized planning. Generalized planning (GP) is usually addressed as a c…

cs.AI2021

Generalized Planning as Heuristic Search

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Plan…

cs.AI2021

Hierarchical Width-Based Planning and Learning

Miquel Junyent, Vicenç Gómez, Anders Jonsson

Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari game…

cs.AI2020★ 21 cited

Induction and Exploitation of Subgoal Automata for Reinforcement Learning

Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2

In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induct…