2 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
Giacomo Arcieri, Gregory Duthé, Christophe Muller +3
Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-level interactions, such as spat…
Deep Belief Markov Models for POMDP Inference
Giacomo Arcieri, Konstantinos G. Papakonstantinou, Daniel Straub +1
This work introduces a novel deep learning-based architecture, termed the Deep Belief Markov Model (DBMM), which provides efficient, model-formulation agnostic inference in Partial…
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance
Giacomo Arcieri, Cyprien Hoelzl, Oliver Schwery +3
Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain environments. A main reason hindering t…
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks
Giacomo Arcieri, David Wölfle, Eleni Chatzi
The need for algorithms able to solve Reinforcement Learning (RL) problems with few trials has motivated the advent of model-based RL methods. The reported performance of model-bas…