使用竞争性多智能体强化学习，通过携带网的无人机拦截敏捷目标
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning
AI总结：
研究用携带捕捉网的无人机团队拦截敏捷无人机的问题，采用带优先虚拟自我博弈的多智能体近端策略优化训练智能体，在高保真模拟器中用低级控制命令训练，结果表明该方案优于基线，且相关因素对追逃任务策略学习至关重要。
摘要：
本文提出了一种由携带捕捉网的敏捷无人机团队拦截敏捷无人机的解决方案。我们将该问题表述为竞争性多智能体强化学习（MARL）任务。为解决智能体过度拟合当前对手策略的非平稳性和灾难性遗忘问题，我们使用带有优先虚拟自我博弈（PFSP）的多智能体近端策略优化（MAPPO）训练追踪者和逃避者。我们在高保真模拟器中使用低级控制命令、集体推力和机体速率（CTBR）训练智能体，以使追踪者和逃避者都能实现敏捷飞行。我们将训练策略在捕获率、捕获时间和坠毁率方面的性能与启发式基线进行比较，结果表明我们的解决方案优于它们。消融研究表明，PFSP导致更稳健的策略，能够适应不同对手策略，且低级控制命令对于在追逃任务中学习执行策略至关重要。最后，对学习行为的定性分析突出了追踪者之间合作策略的出现。
英文摘要：
This article presents a solution to intercept an agile drone by a team of agile drone carrying catching nets. We formulate the problem as a competitive Multi-Agent Reinforcement Learning (MARL) task. To address the problem of nonstationarity and catastrophic forgetting of agents overfitting to the current opponent strategy, we train the pursuers and the evader using Multi-Agent Proximal Policy Optimization (MAPPO) with Prioritized Fictitious Self Play (PFSP). We train the agents in a high-fidelity simulator using low-level control commands, collective thrust and body rates (CTBR), to achieve agile flights for both the pursuers and the evader. We compare the performance of the trained policies in terms of catch rate, time to catch and crash rates, against heuristic baselines and show that our solution outperforms them. Ablation studies show that PFSP lead to more robust policies that can adapt to different opponent strategies, and that a low-level control commands are crucial for learning performing strategies in the pursuit-evasion task. Finally, a qualitative analysis of the learned behaviours highlights the emergence of cooperative tactics among the pursuers.
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。
