62 citations · 151 across the 82 of their papers we have counts for
15 papers · 2 filters
Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity
Davide Maran, Marcello Restelli
We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression…
Statistical Analysis of Policy Space Compression Problem
Majid Molaei, Marcello Restelli, Alberto Maria Metelli +1
Policy search methods are crucial in reinforcement learning, offering a framework to address continuous state-action and partially observable problems. However, the complexity of e…
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing…
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli +1
Policy evaluation via Monte Carlo (MC) simulation is at the core of many MC Reinforcement Learning (RL) algorithms (e.g., policy gradient methods). In this context, the designer of…
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…
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough
Riccardo Zamboni, Duilio Cirino, Marcello Restelli +1
The problem of pure exploration in Markov decision processes has been cast as maximizing the entropy over the state distribution induced by the agent's policy, an objective that ha…