98 citations · 343 across the 26 of their papers we have counts for
35 papers
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…
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…
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…
State Representation Learning for Goal-Conditioned Reinforcement Learning
Lorenzo Steccanella, Anders Jonsson
This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances be…
Hierarchies of Reward Machines
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the…
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
Guillermo Infante, Anders Jonsson, Vicenç Gómez
In this work we present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes. Our approach assumes that the state space is partit…