MM Algorithms to Estimate Parameters in Continuous-time Markov Chains

1 code implementation • 16 Feb 2023 • Giovanni Bacci, Anna Ingólfsdóttir, Kim G. Larsen, Raphaël Reynouard

In this work, we address the problem of estimating parameter values of CTMCs expressed as Prism models from a number of partially-observable executions.

Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)

2 code implementations • 6 Oct 2021 • Giovanni Bacci, Anna Ingólfsdóttir, Kim Larsen, Raphaël Reynouard

Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics.

Active Learning

Approximating Euclidean by Imprecise Markov Decision Processes

no code implementations • 26 Jun 2020 • Manfred Jaeger, Giorgio Bacci, Giovanni Bacci, Kim Guldstrand Larsen, Peter Gjøl Jensen

Second, we use imprecise Markov decision process approximations as a tool to analyse and validate cost functions and strategies obtained by reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

L*-Based Learning of Markov Decision Processes (Extended Version)

no code implementations • 28 Jun 2019 • Martin Tappler, Bernhard K. Aichernig, Giovanni Bacci, Maria Eichlseder, Kim G. Larsen

In this work, we study L*-based learning of deterministic Markov decision processes, first assuming an ideal setting with perfect information.

Active Learning