| Title | Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Song, Lin, Wan, Neng, Gahlawat, Aditya, Hovakimyan, Naira, Theodorou, Evangelos A. |
| Conference Name | 2021 American Control Conference (ACC) |
| Keywords | compositionality, Dynamical Systems, linearity, multi-agent systems, optimal control, pubcrawl, Task Analysis |
| Abstract | 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. |
| DOI | 10.23919/ACC50511.2021.9483210 |
| Citation Key | song_compositionality_2021 |
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems