Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning via Predictive Observation Imputation (Abstract Reprint)
Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo
Abstract
We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents
BibTeX
@inproceedings{aaai2026_centralizedtrain,
title = {Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning via Predictive Observation Imputation (Abstract Reprint)},
author = {Pedro P. Santos and Diogo S. Carvalho and Miguel Vasco and Alberto Sardinha and Pedro A. Santos and Ana Paiva and Francisco S. Melo},
booktitle = {AAAI 2026},
year = {2026}
}