5 citations · 8 across the 11 of their papers we have counts for
12 papers
Adaptive Policy Portfolios for Robust Markov Decision Processes
Kasper Engelen, Sebastian Junges, Guillermo A. Pérez +1
Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partia…
Data Structures for Finite Downsets of Natural Vectors: Theory and Practice
Michaël Cadilhac, Vanessa Flügel, Guillermo A. Pérez +1
Manipulating downward-closed sets of vectors forms the basis of so-called antichain-based algorithms in verification. In that context, the dimension of the vectors is intimately ti…
Revelations: A Decidable Class of POMDPs with Omega-Regular Objectives
Marius Belly, Nathanaël Fijalkow, Hugo Gimbert +3
Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theo…
Algorithms for Markov Binomial Chains
Alejandro Alarcón Gonzalez, Niel Hens, Tim Leys +1
We study algorithms to analyze a particular class of Markov population processes that is often used in epidemiology. More specifically, Markov binomial chains are the model that ar…
Active Learning of Mealy Machines with Timers
Véronique Bruyère, Bharat Garhewal, Guillermo A. Pérez +2
We present the first algorithm for query learning Mealy machines with timers in a black-box context. Our algorithm is an extension of the L# algorithm of Vaandrager et al. to a tim…
Composing Reinforcement Learning Policies, with Formal Guarantees
Florent Delgrange, Guy Avni, Anna Lukina +3
We propose a novel framework to controller design in environments with a two-level structure: a known high-level graph ("map") in which each vertex is populated by a Markov decisio…
Automata with Timers
Véronique Bruyère, Guillermo A. Pérez, Gaëtan Staquet +1
In this work, we study properties of deterministic finite-state automata with timers, a subclass of timed automata proposed by Vaandrager et al. as a candidate for an efficiently l…
Graph-Based Reductions for Parametric and Weighted MDPs
Kasper Engelen, Guillermo A. Pérez, Shrisha Rao
We study the complexity of reductions for weighted reachability in parametric Markov decision processes. That is, we say a state p is never worse than q if for all valuations of th…
Bi-Objective Lexicographic Optimization in Markov Decision Processes with Related Objectives
Damien Busatto-Gaston, Debraj Chakraborty, Anirban Majumdar +3
We consider lexicographic bi-objective problems on Markov Decision Processes (MDPs), where we optimize one objective while guaranteeing optimality of another. We propose a two-stag…
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Florent Delgrange, Ann Nowé, Guillermo A. Pérez
Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios i…
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Raphael Avalos, Florent Delgrange, Ann Nowé +2
Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason takin…
Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability
Michaël Cadilhac, Guillermo A. Pérez
We describe our implementation of downset-manipulating algorithms used to solve the realizability problem for linear temporal logic (LTL). These algorithms were introduced by Filio…