1 citations · 2 across the 5 of their papers we have counts for
7 papers
Differentiating Bisimulation Metrics: A Framework for Parametric Markov Chain Fitting via Bicausal Optimal Transport
Sergio Calo, Amy Zhang, Javier Segovia-Aguas +1
Many problems in sequential decision-making, such as imitation learning from observations, state-space compression, world-model learning, and sim-to-real transfer, can be reduced t…
Robust Successor Features
Erik Nikulski, Yamen Habib, Vicenç Gomez +3
Generalization in Reinforcement Learning (RL) refers to the ability to execute close-to-optimal policies in unseen tasks after the agent has been trained on a different set of task…
Distances for Markov chains from sample streams
Sergio Calo, Anders Jonsson, Gergely Neu +2
Bisimulation metrics are powerful tools for measuring similarities between stochastic processes, and specifically Markov chains. Recent advances have uncovered that bisimulation me…
Parallel Strategies for Best-First Generalized Planning
Alejandro Fernández-Alburquerque, Javier Segovia-Aguas
In recent years, there has been renewed interest in closing the performance gap between state-of-the-art planning solvers and generalized planning (GP), a research area of AI that…
Synthesis of Procedural Models for Deterministic Transition Systems
Javier Segovia-Aguas, Jonathan Ferrer-Mestres, Sergio Jiménez
This paper introduces a general approach for synthesizing procedural models of the state-transitions of a given discrete system. The approach is general in that it accepts differen…
Representation and Synthesis of C++ Programs for Generalized Planning
Javier Segovia-Aguas, Yolanda E-Martín, Sergio Jiménez
The paper introduces a novel representation for Generalized Planning (GP) problems, and their solutions, as C++ programs. Our C++ representation allows to formally proving the term…
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…