  In multi-agent informative path planning (MAIPP), agents must collectively
construct a global belief map of an underlying distribution of interest (e.g.,
gas concentration, light intensity, or pollution levels) over a given domain,
based on measurements taken along their trajectory. They must frequently replan
their path to balance the exploration of new areas with the exploitation of
known high-interest areas, to maximize information gain within a predefined
budget. Traditional approaches rely on reactive path planning conditioned on
other agents' predicted future actions. However, as the belief is continuously
updated, the predicted actions may not match the executed actions, introducing
noise and reducing performance. We propose a decentralized, deep reinforcement
learning (DRL) approach using an attention-based neural network, where agents
optimize long-term individual and cooperative objectives by sharing their
intent, represented as a distribution of medium-/long-term future positions
obtained from their own policy. Intent sharing enables agents to learn to claim
or avoid broader areas, while the use of attention mechanisms allows them to
identify useful portions of imperfect predictions, maximizing cooperation even
based on imperfect information. Our experiments compare the performance of our
approach, its variants, and high-quality baselines across various MAIPP
scenarios. We finally demonstrate the effectiveness of our approach under
limited communication ranges, towards deployments under realistic communication
constraints.
