  We introduce hybrid execution in multi-agent reinforcement learning (MARL), a
new paradigm in which agents aim to successfully 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. To formalize our setting, we define a
new class of multi-agent partially observable Markov decision processes
(POMDPs) that we name hybrid-POMDPs, which explicitly model a communication
process between the agents. We contribute MARO, an approach that makes use of
an auto-regressive predictive model, trained in a centralized manner, to
estimate missing agents' observations at execution time. We evaluate MARO on
standard scenarios and extensions of previous benchmarks tailored to emphasize
the negative impact of partial observability in MARL. Experimental results show
that our method consistently outperforms relevant baselines, allowing agents to
act with faulty communication while successfully exploiting shared information.
