  Mobile network that millions of people use every day is one of the most
complex systems in the world. Optimization of mobile network to meet exploding
customer demand and reduce capital/operation expenditures poses great
challenges. Despite recent progress, application of deep reinforcement learning
(DRL) to complex real world problem still remains unsolved, given data
scarcity, partial observability, risk and complex rules/dynamics in real world,
as well as the huge reality gap between simulation and real world. To bridge
the reality gap, we introduce a Sim-to-Real framework to directly transfer
learning from simulation to real world via graph convolutional neural network
(CNN) - by abstracting partially observable mobile network into graph, then
distilling domain-variant irregular graph into domain-invariant tensor in
locally Euclidean space as input to CNN -, domain randomization and multi-task
learning. We use a novel self-play mechanism to encourage competition among DRL
agents for best record on multiple tasks via simulated annealing, just like
athletes compete for world record in decathlon. We also propose a decentralized
multi-agent, competitive and cooperative DRL method to coordinate the actions
of multi-cells to maximize global reward and minimize negative impact to
neighbor cells. Using 6 field trials on commercial mobile networks, we
demonstrate for the first time that a DRL agent can successfully transfer
learning from simulation to complex real world problem with imperfect
information, complex rules/dynamics, huge state/action space, and multi-agent
interactions, without any training in the real world.
