Deep Reinforcement Learning (DRL) is a powerful framework for solving complex sequential decision-making problems, particularly in robotic control. However, its practical deployment is often hindered by the substantial amount of experience required for learning, which results in high computational and time costs. In this work, we propose a novel integration of DRL with semantic knowledge in the form of Knowledge Graph Embeddings (KGEs), aiming to enhance learning efficiency by providing contextual information to the agent. Our architecture combines KGEs with visual observations, enabling the agent to exploit environmental knowledge during training. Experimental validation with robotic manipulators in environments featuring both fixed and randomized target attributes demonstrates that our method achieves up to {60}{\%} reduction in learning time and improves task accuracy by approximately 15 percentage points, without increasing training time or computational complexity. These results highlight the potential of semantic knowledge to reduce sample complexity and improve the effectiveness of DRL in robotic applications.

翻译：深度强化学习（DRL）是解决复杂序列决策问题的强大框架，尤其在机器人控制领域。然而，其实际部署常因学习所需的大量经验而受阻，导致高昂的计算与时间成本。本研究提出一种将DRL与知识图谱嵌入（KGEs）形式的语义知识相结合的新方法，旨在通过为智能体提供上下文信息来提升学习效率。我们的架构将KGEs与视觉观测相结合，使智能体在训练过程中能够利用环境知识。在具有固定和随机目标属性的环境中对机器人操作臂进行的实验验证表明，该方法能减少高达60%的学习时间，并将任务准确率提升约15个百分点，且未增加训练时间或计算复杂度。这些结果凸显了语义知识在降低样本复杂度、提升DRL在机器人应用中有效性的潜力。