随机微积分指导的强化学习:为最佳决策提供概率框架
Raghavendra M Devadas1, Vani Hiremani2, K R Bhavya3
1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
MethodsX
|July 5, 2024
概括
随机计算引导的强化学习 (SCRL) 提高了不确定性下的决策. 这种新方法的性能优于传统的随机强化学习 (SRL),风险较低,适应性更好.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 决策理论 决策理论
背景情况:
- 在不确定的环境中做出决策是具有挑战性的.
- 传统的随机增强学习 (SRL) 在复杂的场景中存在局限性.
研究的目的:
- 介绍随机计算引导的强化学习 (SCRL) 作为一个先进的决策框架.
- 将SCRL的性能和风险概况与传统SRL方法进行比较.
主要方法:
- 通过将随机微积分原理整合到强化学习中,开发了SCRL.
- 进行实证测试以评估SCRL与SRL相比的适应性,性能和风险.
- 评估指标包括培训奖励,学习进展和滚动平均值.
主要成果:
- 在各种指标上,SCRL表现优于SRL.
- SCRL的分散值 (63.49) 比SRL的分散值 (65.96) 更低.
- 与SRL相比,SCRL的短期 (0.64) 和长期 (0.78) 风险值明显较低 (分别为18.64和10.41).
结论:
- 在不确定的和复杂的情况中,SCRL提供了一种更强大,更低风险的决策方法.
- 这些发现凸显了SCRL在需要谨慎决策的现实应用中的潜力.
- 与传统的SRL方法相比,SCRL是一种显著的进步.
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