用语义知识提升机器人强化学习效率，减少60%训练时间
Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators
- 将知识图谱嵌入与视觉观测结合，让智能体获取环境上下文
- 实验显示学习时间减少60%，任务准确率提升15个百分点
- 适合需要高效训练的机器人控制场景，无需额外计算开销
深度强化学习（DRL）是解决复杂序列决策问题的强大框架，尤其在机器人控制中表现突出。然而，其实际部署常受限于学习所需的大量经验，导致计算和时间成本过高。本文提出一种将语义知识（以知识图谱嵌入，KGEs形式）与DRL结合的新方法，旨在通过向智能体提供环境上下文信息来提升学习效率。我们的架构融合了KGEs与视觉观测，使智能体在训练过程中可利用环境知识。在包含固定与随机目标属性的环境中对机械臂进行实验验证表明，该方法可实现高达60%的学习时间减少，并使任务准确率提升约15个百分点，且不增加训练时间或计算复杂度。结果表明，语义知识有助于降低样本复杂性，显著提升DRL在机器人应用中的有效性。
原文摘要 · Abstract (English)
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.
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