  A communication enabled indoor intelligent robots (IRs) service framework is
proposed, where non-orthogonal multiple access (NOMA) technique is adopted to
enable highly reliable communications. In cooperation with the ultramodern
indoor channel model recently proposed by the International Telecommunication
Union (ITU), the Lego modeling method is proposed, which can deterministically
describe the indoor layout and channel state in order to construct the radio
map. The investigated radio map is invoked as a virtual environment to train
the reinforcement learning agent, which can save training time and hardware
costs. Build on the proposed communication model, motions of IRs who need to
reach designated mission destinations and their corresponding down-link power
allocation policy are jointly optimized to maximize the mission efficiency and
communication reliability of IRs. In an effort to solve this optimization
problem, a novel reinforcement learning approach named deep transfer
deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation
results demonstrate that 1) With the aid of NOMA techniques, the communication
reliability of IRs is effectively improved; 2) The radio map is qualified to be
a virtual training environment, and its statistical channel state information
improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is
superior to the conventional deep deterministic policy gradient (DDPG)
algorithm in terms of optimization performance, training time, and anti-local
optimum ability.
