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Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known ...

Multi-agent reinforcement learning addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world scenarios, agents not only aim to maximize their goals but also need to ensure safe ...

We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash equilibrium policy according to some unknown payoff function. IL in MFGs ...

Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the ...

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a ...

Graph Neural Networks (GNNs) have recently achieved good performance in many predictive tasks involving graph-structured data. However, the majority of existing models consider static graphs only and do not support training on graph streams. While ...

The Configurable Markov Decision Process framework includes two entities: a Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance. This presupposes that the two actors have ...

In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we ...

Inverse Reinforcement Learning addresses the problem of inferring an expert's reward function from demonstrations. However, in many applications, we not only have access to the expert's near-optimal behaviour, but we also observe part of her learning ...

In this paper, we propose two approaches to the problem of finding similar users to a set of champions representing domains of interest on social media. The first approach is based on the content shared by the users, while the second one relies on the ...

With the increase of digital interaction, social networks are becoming an essential ingredient of our life, by progressively becoming the dominant media, e.g. in influencing political choices. Interaction within social networks tends to take place within ...

Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once ...

This demo presents our experiences in developing a joystick for videogames that uses RFID backscattering for battery-free operation. Specifically, we develop a system to gather data from a wireless and battery-less joystick, named JoyTag, while it ...