论文标题
基于Q的深度学习路径计划和消防环境的导航系统
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments
论文作者
论文摘要
Live Fire创造了一个动态，快速变化的环境，为深度学习和人工智能方法论提供了一个值得挑战的挑战，可以帮助消防员对场景理解，以保持其情境意识，跟踪和继电器对关键决策所必需的重要特征，因为他们应对这些灾难性事件。我们提出了一个基于Q的深度学习药物，该药物可以免疫压力引起的迷失方向和焦虑，因此能够根据在实时火灾环境中观察到的事实做出明确的导航决定。作为概念证明，我们模仿了称为虚幻引擎的游戏引擎中的结构火，该引擎可以使代理与环境的相互作用。根据对环境的行动，根据一系列奖励和处罚，对代理商进行了深入学习算法的培训。我们利用经验重播以加快学习过程并通过人类衍生的经验来增强代理商的学习。在这种深度Q学习方法下训练的代理商的表现优于通过替代路径计划系统训练的代理商，并将这种方法作为有前途的基础，以建立能够通过实时火灾环境安全指导消防员的路径规划助手。
Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for navigation based on the observed and stored facts in live fire environments. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine which enables the interaction of the agent with the environment. The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences. The agent trained under this deep Q-learning approach outperforms agents trained through alternative path planning systems and demonstrates this methodology as a promising foundation on which to build a path planning navigation assistant capable of safely guiding fire fighters through live fire environments.
