3 citations · 8 across the 23 of their papers we have counts for
5 papers · 1 filter
Understanding Deep Neural Function Approximation in Reinforcement Learning via -Greedy Exploration
Fanghui Liu, Luca Viano, Volkan Cevher
This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the -greedy exploration under the online setting. This problem…
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
Paul Rolland, Luca Viano, Norman Schuerhoff +2
While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function fr…
Proximal Point Imitation Learning
Luca Viano, Angeliki Kamoutsi, Gergely Neu +2
This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence…
Robust Learning from Observation with Model Misspecification
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban +3
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorith…
Neural NID Rules
Luca Viano, Johanni Brea
Abstract object properties and their relations are deeply rooted in human common sense, allowing people to predict the dynamics of the world even in situations that are novel but g…