  In this paper, we discuss the methodology of generalizing the optimal control
law from learned component tasks to unlearned composite tasks on Multi-Agent
Systems (MASs), by using the linearity composition principle of linearly
solvable optimal control (LSOC) problems. The proposed approach achieves both
the compositionality and optimality of control actions simultaneously within
the cooperative MAS framework in both discrete- and continuous-time in a
sample-efficient manner, which reduces the burden of re-computation of the
optimal control solutions for the new task on the MASs. We investigate the
application of the proposed approach on the MAS with coordination between
agents. The experiments show feasible results in investigated scenarios,
including both discrete and continuous dynamical systems for task
generalization without resampling.
