具有风险意识的自主搜索和救援与多代理强化学习
Aowabin Rahman1, Salman Shuvo1, Samrat Chatterjee2
1Optimization and Control Group, Pacific Northwest National Laboratory, Richland, Washington, USA.
概括
本研究介绍了自主搜索和救援 (SAR) 机器人在不确定的环境中导航的风险意识多代理强化学习方法. 该方法解决了对抗性风险和不断变化的机器人导航安全条件.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 强化学习是一种强化学习.
背景情况:
- 在动态,高后果的环境中,如搜救 (SAR) 任务中,自动导航至关重要.
- 多代理机器人系统面临着适应不断变化的条件和对抗风险的挑战.
- 学习行为的偏差和不断变化的环境障碍会影响自主代理的性能.
研究的目的:
- 为自主SAR开发一种具有风险意识的多代理强化学习方法.
- 以数学形式制定自主SAR问题,考虑对抗性和环境不确定性.
- 在各种危险场景中评估拟议的方法.
主要方法:
- 制定了自主SAR问题,使用风险意识的多代理强化学习框架.
- 设计和实施了用于评估的数值实验.
- 采用了分散测试 (CTDE) 范式的集中培训.
主要成果:
- 风险意识多剂强化学习方法在处理动态和不确定的SAR环境方面表现出有效性.
- 该CTDE范式促进了在对抗条件下自主代理的强有力的性能.
- 实验结果验证了该方法适应不断变化的障碍的能力.
结论:
- 提出的具有风险意识的多代理强化学习方法为增强SAR任务中的自主导航提供了有希望的解决方案.
- 需要进一步的研究来探索先进的适应策略和现实世界的部署.
- 该研究强调了风险意识在高后果应用的多剂体系统中的重要性.
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