2 citations · 2 across the 10 of their papers we have counts for
12 papers
ICMAPE: In-Context Multiagent Pure Exploration
Xinyi Hu, Alessio Russo, Aldo Pacchiano
In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of the environment as done in…
Future Information-Directed Sampling for Bayesian Nonstationary Bandits
Yichen Song, Alessio Russo, Aldo Pacchiano
Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Sampling effectively balance thi…
Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
Joseph Lazzaro, Alessio Russo, Aldo Pacchiano
In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning. This is an active sequential hypothesis testing problem in which the l…
Receding-Horizon Control via Drifting Models
Daniele Foffano, Alessio Russo, Alexandre Proutiere
We study the problem of trajectory optimization in settings where the system dynamics are unknown and it is not possible to simulate trajectories through a surrogate model. When an…
In-Context Pure Exploration in Continuous Decision Spaces
Alessio Russo, Yin-Ching Lee, Ryan Welch +1
In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis…
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
Davide Salaorni, Vincenzo De Paola, Samuele Delpero +9
In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves towa…
In-Context Learning for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
We study the problem active sequential hypothesis testing, also known as pure exploration: given a new task, the learner adaptively collects data from the environment to efficientl…
Pure Exploration with Feedback Graphs
Alessio Russo, Yichen Song, Aldo Pacchiano
We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenario…
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
Alessio Russo, Aldo Pacchiano
We study the policy evaluation problem in an online multi-reward multi-policy discounted setting, where multiple reward functions must be evaluated simultaneously for different pol…
Achieving Regret in Average-Reward POMDPs with Known Observation Models
Alessio Russo, Alberto Maria Metelli, Marcello Restelli
We tackle average-reward infinite-horizon POMDPs with an unknown transition model but a known observation model, a setting that has been previously addressed in two limiting ways:…
Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting
Alessio Russo, Alberto Maria Metelli, Marcello Restelli
Dealing with Partially Observable Markov Decision Processes is notably a challenging task. We face an average-reward infinite-horizon POMDP setting with an unknown transition model…
Model-Free Active Exploration in Reinforcement Learning
Alessio Russo, Alexandre Proutiere
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-spe…