1 citations · 1 across the 13 of their papers we have counts for
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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…
K-Myriad: Jump-starting reinforcement learning with unsupervised parallel agents
Vincenzo De Paola, Mirco Mutti, Riccardo Zamboni +1
Parallelization in Reinforcement Learning is typically employed to speed up the training of a single policy, where multiple workers collect experience from an identical sampling di…
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…