no code implementations • 8 Jan 2024 • Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli, Marcello Restelli
For this reason, in this work, we extend the IRL formulation to problems where, in addition to demonstrations from the optimal agent, we can observe the behavior of multiple sub-optimal experts.
no code implementations • 29 Aug 2023 • Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
In this setting, the agent's goal lies in sequentially choosing which mediator to query to identify with high probability the optimal arm while minimizing the identification time, i. e., the sample complexity.
no code implementations • 7 May 2023 • Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i. e., the expected return.
no code implementations • 25 Jul 2022 • Riccardo Poiani, Ciprian Stirbu, Alberto Maria Metelli, Marcello Restelli
With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios.
1 code implementation • 18 May 2021 • Riccardo Poiani, Andrea Tirinzoni, Marcello Restelli
At test time, TRIO tracks the evolution of the latent parameters online, hence reducing the uncertainty over future tasks and obtaining fast adaptation through the meta-learned policy.
no code implementations • ICML 2020 • Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli
We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones.