... of the model and propose a data-based approach for its optimal control, along with a comparison to the control using a state-of-the-art reinforcement learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such microstructures to attain desired material properties and complex...

...Chao Jia; Zijian Song; Lifeng Du; Hongkun Wang Considering the load uncertainty and unmodeled dynamics in multicylinder hydraulic systems, this paper proposes a balance control algorithm based on safe reinforcement learning to release the restrictions of classical model-based control methods...

... detection and reconstruction reinforcement learning The multi-agent systems (MAS) have garnered substantial attention from both the academic and industrial sectors [ 1 ]. As MAS continue to grow in scale and complexity [ 2 ], the presence of any kind of faults within the system can significantly...