安全强化学习的审查:方法,理论和应用
IEEE transactions on pattern analysis and machine intelligence
|September 10, 2024
概括
本综述探讨了安全的强化学习 (RL) 方法,理论和应用. 它解决了现实世界部署的关键挑战,并讨论了更安全的人工智能系统的未来研究方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 强化学习 (RL) 在决策方面表现出色,但在自动驾驶和机器人等现实应用中面临安全问题.
- 安全的强化学习 (RL) 算法领域正在芽,尽管在安全控制方面有很长的历史.
研究的目的:
- 提供安全RL算法的全面审查,涵盖方法,理论和应用.
- 建立一个基础的理解,以未来的研究安全的RL.
- 识别和解决在现实世界中部署安全RL的关键挑战.
主要方法:
- 在五个维度中审查安全RL的进展.
- 分析算法和理论,以应对 "2H3W" 挑战 (现实世界部署的五个关键问题).
- 讨论安全RL算法的样本复杂性,应用和基准.
主要成果:
- 确定五个关键问题 ("2H3W"),阻碍现实世界安全的RL部署.
- 分析与这些挑战相关的当前安全RL算法和理论.
- 对样本复杂性,应用和基准的审查.
结论:
- 安全RL对于现实世界的应用至关重要,需要对其方法和理论进行进一步的研究.
- 应对 "2H3W" 挑战是推动安全 RL 的关键.
- 发布了主要安全RL算法的开源存储库,以促进研究.
相关概念视频
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Law of Effect
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In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
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Operant Conditioning Intervention
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In operant conditioning, behaviors that are...
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