In complex systems, we often observe complex global behavior emerge from a
collection of agents interacting with each other in their environment, with
each individual agent acting only on locally available information, without
knowing the full picture. Such systems have inspired development of artificial
intelligence algorithms in areas such as swarm optimization and cellular
automata. Motivated by the emergence of collective behavior from complex
cellular systems, we build systems that feed each sensory input from the
environment into distinct, but identical neural networks, each with no fixed
relationship with one another. We show that these sensory networks can be
trained to integrate information received locally, and through communication
via an attention mechanism, can collectively produce a globally coherent
policy. Moreover, the system can still perform its task even if the ordering of
its inputs is randomly permuted several times during an episode. These
permutation invariant systems also display useful robustness and generalization
properties that are broadly applicable. Interactive demo and videos of our
results: this https URL

基于神经网络和注意机制的复杂系统信息整合研究和探索。