  We investigate a classification problem using multiple mobile agents capable
of collecting (partial) pose-dependent observations of an unknown environment.
The objective is to classify an image over a finite time horizon. We propose a
network architecture on how agents should form a local belief, take local
actions, and extract relevant features from their raw partial observations.
Agents are allowed to exchange information with their neighboring agents to
update their own beliefs. It is shown how reinforcement learning techniques can
be utilized to achieve decentralized implementation of the classification
problem by running a decentralized consensus protocol. Our experimental results
on the MNIST handwritten digit dataset demonstrates the effectiveness of our
proposed framework.
