基于带通信的多智能体强化学习的多船舶协同导航与避碰
Multiple Ships Cooperative Navigation and Collision Avoidance using Multi-agent Reinforcement Learning with Communication
AI总结：
针对多USV协同控制中的非平稳与部分可观测性难题，提出带通信的MADDPG算法，在gym环境的协同导航与避碰任务中性能优于传统单智能体算法，可通过通信协议共享信息实现更好协同。
摘要：
在现实世界中，无人水面艇（USV）通常需要相互协作以完成特定任务。然而，由于非平稳性和部分可观测性等问题，在多智能体系统中实现协同控制颇具挑战。多智能体强化学习（MARL）的最新进展为应对这些挑战提供了新的视角。因此，我们提出采用带通信的多智能体深度确定性策略梯度（MADDPG）算法，来解决部分可观测性下的多船舶协同问题。我们基于OpenAI的gym环境开发了两项任务：协同导航与协同避碰。在这些任务中，船舶不仅要学习有效的控制策略，还必须与其他智能体建立通信协议。我们分析了外部噪声对通信的影响、智能体间通信对性能的作用，以及智能体学习到的通信模式。结果表明，我们提出的框架可有效解决多船舶间的协同导航与避碰问题，性能显著优于传统的单智能体算法。智能体建立了一致的通信协议，能够通过共享观测信息弥补缺失的信息，实现更好的协同。
英文摘要：
In the real world, unmanned surface vehicles (USV) often need to coordinate with each other to accomplish specific tasks. However, achieving cooperative control in multi-agent systems is challenging due to issues such as non-stationarity and partial observability. Recent advancements in Multi-Agent Reinforcement Learning (MARL) provide new perspectives to address these challenges. Therefore, we propose using the multi-agent deep deterministic policy gradient (MADDPG) algorithm with communication to address multiple ships' cooperation problems under partial observability. We developed two tasks based on OpenAI's gym environment: cooperative navigation and cooperative collision avoidance. In these tasks, ships must not only learn effective control strategies but also establish communication protocols with other agents. We analyze the impact of external noise on communication, the effect of inter-agent communication on performance, and the communication patterns learned by the agents. The results demonstrate that our proposed framework effectively addresses cooperative navigation and collision avoidance among multiple vessels, significantly outperforming traditional single-agent algorithms. Agents establish a consistent communication protocol, enabling them to compensate for missing information through shared observations and achieve better coordination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
