  Mobile ad hoc computing (MAHC), which allows mobile devices to directly share
their computing resources, is a promising solution to address the growing
demands for computing resources required by mobile devices. However, offloading
a computation task from a mobile device to other mobile devices is a
challenging task due to frequent topology changes and link failures because of
node mobility, unstable and unknown communication environments, and the
heterogeneous nature of these devices. To address these challenges, in this
paper, we introduce a novel coded computation scheme based on multi-agent
reinforcement learning (MARL), which has many promising features such as
adaptability to network changes, high efficiency and robustness to uncertain
system disturbances, consideration of node heterogeneity, and decentralized
load allocation. Comprehensive simulation studies demonstrate that the proposed
approach can outperform state-of-the-art distributed computing schemes.
