Search Results for author: Riccardo Poiani

Found 6 papers, 1 papers with code

Inverse Reinforcement Learning with Sub-optimal Experts

no code implementations8 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.

reinforcement-learning

Pure Exploration under Mediators' Feedback

no code implementations29 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.

Decision Making Multi-Armed Bandits

Truncating Trajectories in Monte Carlo Reinforcement Learning

no code implementations7 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.

reinforcement-learning Reinforcement Learning (RL)

Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs

no code implementations25 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.

Meta-Reinforcement Learning by Tracking Task Non-stationarity

1 code implementation18 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.

Meta Reinforcement Learning reinforcement-learning +1

Sequential Transfer in Reinforcement Learning with a Generative Model

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.

reinforcement-learning Reinforcement Learning (RL)

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