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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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    强有力的强化学习 (RRL) 现在扩展到连续空间. 新的算法通过优化最坏情况下的性能来确保强大的政策,克服表式方法的局限性.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 控制理论 控制理论

    背景情况:

    • 强有力的强化学习 (RRL) 在马尔科夫决策过程 (MDP) 的不确定性集中优化了针对最坏情景的政策.
    • 目前的RRL方法仅限于表格设置,阻碍了在复杂的连续状态/动作空间中的应用.
    • 现实世界的部署面临挑战,因为培训模拟与实际环境之间的潜在不匹配.

    研究的目的:

    • 将强化的强化学习算法扩展到连续状态和动作空间.
    • 开发一种新的RRL方法,保证与现有方法相比提高了稳定性.
    • 解决当前RRL技术在处理复杂环境中的局限性.

    主要方法:

    • 构建了一个详细的不确定性集,可信的扰乱的MDPs.
    • 建议用于具有有限时间误差限制的表格设置的相邻强大的Q学习 (ARQ学习).
    • 引入了一种带有悲观因素的双代理方法,以使扩展到连续空间.

    主要成果:

    • ARQ-Learning显示了与Q-learning和Robust-Q相似的趋同,具有增强的稳定性保证.
    • 这种新的双代理方法首次成功地将无模型的RRL扩展到连续状态/动作空间.
    • 实验验证证了在连续环境中提出的算法的有效性.

    结论:

    • 开发的RRL算法有效地处理连续状态/动作空间,这与之前的工作相比是显著的进步.
    • 双代理方法为优化复杂,不确定的环境中的政策提供了强大的解决方案.
    • 这项研究为在现实世界应用中应用强化学习 (robust reinforcement learning) 开辟了新的途径.