2 citations · 2 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…