88 citations · 352 across the 96 of their papers we have counts for
103 papers
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds
Gabor Paczolay, Matteo Papini, Alberto Maria Metelli +2
Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved sample complexity to find an -stationary point, under an unrealistic…
Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Giovanni Dispoto, Marcello Restelli, Carmine Ventre
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinfo…
Online Market Making and the Value of Observing the Order Book
Davide Maran, Marcello Restelli
We study an online market-making problem in which a learner sequentially posts bid and ask prices for a single asset while interacting with traders holding private valuations. Unli…
Actor-Critic with Active Importance Sampling
Majid Molaei, Gabor Paczolay, Matteo Papini +2
This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in policy gradient estimation.…
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
Andrea Fraschini, Davide Tenedini, Riccardo Zamboni +2
Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inh…
How Log-Barrier Helps Exploration in Policy Optimization
Leonardo Cesani, Matteo Papini, Marcello Restelli
Recently, it has been shown that the Stochastic Gradient Bandit (SGB) algorithm converges to a globally optimal policy with a constant learning rate. However, these guarantees rely…