  We study the problem of multi-robot mapless navigation in the popular
Centralized Training and Decentralized Execution (CTDE) paradigm. This problem
is challenging when each robot considers its path without explicitly sharing
observations with other robots and can lead to non-stationary issues in Deep
Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint
action-value function into individual ones, to favor cooperation and achieve
decentralized execution. Such factorization involves constraints (e.g.,
monotonicity) that limit the emergence of novel behaviors in an individual as
each agent is trained starting from a joint action-value. In contrast, we
propose a novel architecture for CTDE that uses a centralized state-value
network to compute a joint state-value, which is used to inject global state
information in the value-based updates of the agents. Consequently, each model
computes its gradient update for the weights, considering the overall state of
the environment. Our idea follows the insights of Dueling Networks as a
separate estimation of the joint state-value has both the advantage of
improving sample efficiency, while providing each robot information whether the
global state is (or is not) valuable. Experiments in a robotic navigation task
with 2 4, and 8 robots, confirm the superior performance of our approach over
prior CTDE methods (e.g., VDN, QMIX).
