弥合适应性管理和强化学习的桥梁,以获得更强大的决策
Melissa Chapman1, Lily Xu2, Marcus Lapeyrolerie1
1Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA.
概括
人工智能,特别是强化学习 (RL),通过从经验中学习,为环境管理提供了一种新的方法. 这种方法可以改善传统优化失败的复杂,不确定的系统中的决策.
科学领域:
- 人工智能的人工智能
- 环境管理环境管理
- 生物多样性科学 生物多样性科学
背景情况:
- 人工智能 (AI) 方法擅长在不确定的环境中进行复杂的决策.
- 适应性环境管理旨在通过经验和最新的知识来改进战略.
研究的目的:
- 探索强化学习 (RL) 的应用,AI的一个子领域,环境管理.
- 评估RL在不确定性下改进基于证据的适应性管理决策的潜力.
主要方法:
- 审查强化学习原则及其与适应性环境管理的相似之处.
- 在经典优化方法难以解决的情况下,分析RL的适用性.
- 讨论将RL应用于环境系统的技术和社会挑战.
主要成果:
- 强化学习 (RL) 为适应性环境管理提供了一个有希望的框架.
- 在高维度,不确定的环境系统中,RL可以增强决策.
- 确定了在这个领域实施RL的技术和社会考虑因素.
结论:
- 环境管理和计算机科学可以从理解基于经验的决策中相互受益.
- RL为改进适应性环境管理策略提供了一个有价值的镜头.
- 鼓励进一步的跨学科合作,以利用人工智能应对环境挑战.
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