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Attaining global optimized solution by applying Q-learning

The assembly of modular construction projects depends primarily on the use of heavy capacity cranes. The increase in usage of heavy cranes is the impetus for the optimization of resources utilized for the crane work. One of the major cost drivers is the design optimization process of such resources. Even for an initial mechanical/structural design prior to a prototype, the design phase with finite element analysis (FEA) is time-consuming and extensive work. The greedy algorithm could be an answer to these problems to accomplish the optimization in short period of time. In some of the complex cases, the greedy algorithm can confine to the local optimum and thus, overlooking the global optimum. To avoid this, a model-free reinforcement learning (RL) algorithm, Q-learning, is employed in this study to evaluate its suitability for use in the design optimization process with the use of FEA. A hypothetical structural support design problem is used to formulate a framework for comparing the greedy algorithm and Q-learning. The findings show that not only can Q-learning overcome the local optimum confinement of the greedy algorithm, but it in fact can surpass the greedy algorithm along the progressive iteration, by refining policy and reward.