Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme
研究了未知动态非线性多智能体系统的二分一致性问题，利用T-S模糊模型和零和博弈将其转化为求解博弈代数Riccati方程，并提出一种缩放离策略迭代算法，无需初始可行控制策略且收敛更快。
The bipartite consensus (BC) issue for nonlinear multiagent systems (NMASs) with unknown system dynamics information is investigated in this article. Initially, the dynamics of NMASs are represented using the Takagi-Sugeno (T-S) fuzzy model. Subsequently, to achieve distributed control, a minmax game policy is introduced, where each agent aims to minimize its performance index while its neighbors attempt to maximize it. Consequently, the BC problem for NMASs is reformulated as a zero-sum game, transforming the controller design into solving a set of game algebraic Riccati equations (GAREs). To solve such equations, a novel scaling off-policy iteration (PI) algorithm is proposed. The key features of the proposed learning algorithm can be outlined in three main aspects: 1) during the learning process, the reliance on system dynamics is relaxed; 2) compared with the PI method, the requirement for initial admissible control policies is eliminated; and 3) a more rapid convergence speed is achieved than traditional value iteration. Finally, the effectiveness and advantages of the proposed method are validated through a simulation example and a series of comparative experiments.