Active structural control framework using policy-gradient reinforcement learning

This paper presents a novel data-driven approach for active structural control through the use of deep reinforcement learning, wherein, the control system learns to react in an optimal manner through a training process that utilizes deep neural networks within a reinforcement learning framework. The key advantage of this paradigm is the data-driven approach to active control which helps circumvent the need for high-fidelity modeling that typically requires extensive prior knowledge about the structure of interest. Furthermore, the proposed framework is applicable for designing a variety of active controllers, and different external load types, for example, wind and seismic loads for any desired building. The efficacy of the proposed framework is demonstrated in the context of seismic response control through three numerical case studies. The results confirm that the proposed approach yields significant structural response reductions in the linear and nonlinear regimes. Furthermore, implementation issues such as sensitivity to structural property variations, and time delay are thoroughly investigated.