3 citations · 3 across the 9 of their papers we have counts for
9 papers
Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Filippo Lazzati, Kyle Stachowicz, William Chen +3
Action chunking---predicting and executing multiple actions instead of a single action---has proven to be a critical component for learning effective robotic control policies. Howe…
Imitation Learning as Return Distribution Matching
Filippo Lazzati, Alberto Maria Metelli
We study the problem of training a risk-sensitive reinforcement learning (RL) agent through imitation learning (IL). Unlike standard IL, our goal is not only to train an agent that…
Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
Filippo Lazzati, Alberto Maria Metelli
We study the problem of generalizing an expert agent's behavior, provided through demonstrations, to new environments and/or additional constraints. Inverse Reinforcement Learning…
Reward Compatibility: A Framework for Inverse RL
Filippo Lazzati, Mirco Mutti, Alberto Metelli
We provide an original theoretical study of Inverse Reinforcement Learning (IRL) through the lens of reward compatibility, a novel framework to quantify the compatibility of a rewa…
Robustness in the Face of Partial Identifiability in Reward Learning
Filippo Lazzati, Alberto Maria Metelli
In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application…
Learning Utilities from Demonstrations in Markov Decision Processes
Filippo Lazzati, Alberto Maria Metelli
Our goal is to extract useful knowledge from demonstrations of behavior in sequential decision-making problems. Although it is well-known that humans commonly engage in risk-sensit…
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
In online Inverse Reinforcement Learning (IRL), the learner can collect samples about the dynamics of the environment to improve its estimate of the reward function. Since IRL suff…
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
Inverse reinforcement learning (IRL) aims to recover the reward function of an expert agent from demonstrations of behavior. It is well-known that the IRL problem is fundamentally…
Towards Theoretical Understanding of Inverse Reinforcement Learning
Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli
Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a reward function justifying the behavior demonstrated by an expert agent. A well-known…