深度强化学习在水下目标识别中的应用研究

Research of Deep Reinforcement Learning Applied in Underwater Target Recognition

深度强化学习的发展标志着人工智能领域的一次革命性进步。它结合了深度学习和强化学习的技术,使智能体能够在复杂、未知的环境中不断改进和优化自己的行为。论文首先对强化学习及深度强化学习相关的主流算法进行了综述,剖析了现有方法的优点和局限性。进一步地,文章详细分析了深度强化学习在军事领域的具体应用,重点聚焦于水下目标识别方向,并为实际推进深度强化学习技术在军事领域应用落地所面临的一系列问题和挑战进行了全面评估,旨在促进这一技术在军事领域的可持续发展,为未来相关研究和实践提供了有力的参考。

The rapid advancement of deep reinforcement learning signifies a revolutionary progress in the field of artificial in-telligence.By amalgamating the technologies of deep learning and reinforcement learning,it empowers intelligent agents to continu-ously refine and optimize their behaviors in intricate and unfamiliar environments.This article commences with an overview of main-stream algorithms related to reinforcement learning and deep reinforcement learning,dissecting the merits and limitations of existing methods.Furthermore,it meticulously scrutinizes the specific applications of deep reinforcement learning in the military domain,with a particular emphasis on underwater target identification.The analysis extends to a comprehensive evaluation of the practical challenges and issues faced in implementing deep reinforcement learning technologies within military applications.The overarching goal is to stimulate the sustainable development of this technology in the military domain,offering a robust reference for future re-search and practical endeavors.