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…