2021 article
Decomposability and Parallel Computation of Multi-Agent LQR
2021 AMERICAN CONTROL CONFERENCE (ACC), pp. 4527–4532.
author keywords: Reinforcement learning; linear quadratic regulator; multi-agent systems; decomposition
TL;DR:
This work proposes a parallel RL scheme for a linear quadratic regulator (LQR) design in a continuous-time linear MAS and shows that if the MAS is homogeneous then this decomposition retains closed-loop optimality.
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arxiv.org (repository)
Source: Web Of Science
Added: November 1, 2021
Individual agents in a multi-agent system (MAS) may have decoupled open-loop dynamics, but a cooperative control objective usually results in coupled closed-loop dynamics thereby making the control design computationally expensive. The computation time becomes even higher when a learning strategy such as reinforcement learning (RL) needs to be applied to deal with the situation when the agents dynamics are not known. To resolve this problem, we propose a parallel RL scheme for a linear quadratic regulator (LQR) design in a continuous-time linear MAS. The idea is to exploit the structural properties of two graphs embedded in the $Q$ and $R$ weighting matrices in the LQR objective to define an orthogonal transformation that can convert the original LQR design to multiple decoupled smaller-sized LQR designs. We show that if the MAS is homogeneous then this decomposition retains closed-loop optimality. Conditions for decomposability, an algorithm for constructing the transformation matrix, a parallel RL algorithm, and robustness analysis when the design is applied to non-homogeneous MAS are presented. Simulations show that the proposed approach can guarantee significant speed-up in learning without any loss in the cumulative value of the LOR cost.