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Deep reinforcement learning (RL) is an advancing learning tool to handle robotics control problems. However, it typically suffers from sample efficiency and effectiveness. The emergence of Graph Neural Networks (GNNs) enables the integration of the RL and graph representation learning techniques. It realises outstanding training performance and transfer capability by forming controlling scenarios into the corresponding graph domain. Nevertheless, the existing approaches strongly depend on the artificial graph formation processes with intensive bias and cannot propagate messages discriminatively on explicit physical dependence, which leads to restricted flexibility, size transfer capability and suboptimal performance. This paper proposes a fuzzy attention mechanism-based heterogeneous graph neural network (FAM-HGNN) framework for resolving the control problem under the RL context. FAM emphasises …