The paper presents a reinforcement learning method, HSAC, that builds covering structures without relying on rigid, pre‑planned sequences. It uses graph‑structured state representations and a mixed action space to select blocks and adjust their placement continuously, while an efficient exploration strategy incorporates unilateral edges into graph neural networks. HSAC outperforms the prior hybrid‑PPO approach, shows strong sample efficiency, robustness to hyperparameters, and successfully transfers policies from simulation to a real two‑robot 3D‑printed block construction task.
The paper presents HSAC, a reinforcement learning method that builds covering structures without predefined plans, using graph-structured states and a mixed action space of discrete block selection and continuous placement. It extends soft actor-critic with unilateral edges in graph neural networks to efficiently explore while simulating stability. Experiments show HSAC outperforms hybrid-PPO, remains robust to hyperparameters, and successfully transfers to a real two-robot 3D‑printed arch construction.
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