307 citations · 1.8k across the 57 of their papers we have counts for
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CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making
Giovanni Minelli, Mirco Musolesi
Robust coordination skills enable agents to operate cohesively in shared environments, together towards a common goal and, ideally, individually without hindering each other's prog…
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
Giorgio Franceschelli, Mirco Musolesi
Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged a…
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning
Lorenz Wolf, Mirco Musolesi
Existing modular Reinforcement Learning (RL) architectures are generally based on reusable components, also allowing for "plug-and-play" integration. However, these modules are hom…
CT-DQN: Control-Tutored Deep Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo +2
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Contr…
Graph Neural Modeling of Network Flows
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics. Among them, t…
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning
Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes +1
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical…