1 citations · 1 across the 11 of their papers we have counts for
11 papers
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5
Offline multi-agent reinforcement learning (MARL) enables policy learning from fixed datasets, but is prone to coordination failure: agents trained on static, off-policy data conve…
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…
Probing Dec-POMDP Reasoning in Cooperative MARL
Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni +2
Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stem…
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…
Remembering the Markov Property in Cooperative MARL
Kale-ab Abebe Tessera, Leonard Hinckeldey, Riccardo Zamboni +2
Cooperative multi-agent reinforcement learning (MARL) is typically formalised as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP), where agents must reason…
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…
Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information
Riccardo Zamboni, Enrico Brunetti, Marcello Restelli
Offline Reinforcement Learning (RL) focuses on learning policies solely from a batch of previously collected data. offering the potential to leverage such datasets effectively with…
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…
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough
Riccardo Zamboni, Duilio Cirino, Marcello Restelli +1
The problem of pure exploration in Markov decision processes has been cast as maximizing the entropy over the state distribution induced by the agent's policy, an objective that ha…
How to Explore with Belief: State Entropy Maximization in POMDPs
Riccardo Zamboni, Duilio Cirino, Marcello Restelli +1
Recent works have studied *state entropy maximization* in reinforcement learning, in which the agent's objective is to learn a policy inducing high entropy over states visitation (…