3 citations · 11 across the 26 of their papers we have counts for
7 papers · 1 filter
Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames
Ev Zisselman, Mirco Mutti, Shelly Francis-Meretzki +2
Behavioral cloning is a simple yet effective technique for learning sequential decision-making from demonstrations. Recently, it has gained prominence as the core of foundation mod…
From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti +1
Despite its recent successes, Deep Reinforcement Learning (DRL) is notoriously sample-inefficient. We argue that this inefficiency stems from the standard practice of optimizing po…
Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story
Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti +1
Parallel data collection has redefined Reinforcement Learning (RL), unlocking unprecedented efficiency and powering breakthroughs in large-scale real-world applications. In this pa…
State Entropy Regularization for Robust Reinforcement Learning
Yonatan Ashlag, Uri Koren, Mirco Mutti +3
State entropy regularization has empirically shown better exploration and sample complexity in reinforcement learning (RL). However, its theoretical guarantees have not been studie…
A Classification View on Meta Learning Bandits
Mirco Mutti, Jeongyeol Kwon, Shie Mannor +1
Contextual multi-armed bandits are a popular choice to model sequential decision-making. E.g., in a healthcare application we may perform various tests to asses a patient condition…
Towards Principled Unsupervised Multi-Agent Reinforcement Learning
Riccardo Zamboni, Mirco Mutti, Marcello Restelli
In reinforcement learning, we typically refer to unsupervised pre-training when we aim to pre-train a policy without a priori access to the task specification, i.e. rewards, to be…
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…