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Recently, it was demonstrated that the design synthesis of truss structures can be modeled as a Markov decision process (MDP) and solved using a tabular reinforcement learning method. In this setting, each state corresponds to a specific design configuration represented as a finite graph. However, when the structural design domain is relatively large, and depending on the constraints, the dimensionality of the state space becomes quite large rendering tabular reinforcement learning algorithms inefficient. Hence, in this study, the design synthesis MDP framework is significantly extended to solve structural design problems with large state spaces, by integrating deep reinforcement learning (DRL) into the general MDP framework. This is beneficial because with DRL, a deep neural network can be used to approximate the state-action value function, such that the network has much fewer parameters than the …