This paper considers the problem of real-time control and learning in dynamic systems subjected to uncertainties. Adaptive approaches are proposed to address the problem, which are combined to with methods and tools in Reinforcement Learning (RL) and Machine Learning (ML). Algorithms are proposed in continuous-time that combine adaptive approaches with RL leading to online control policies that guarantee stable behavior in the presence of parametric uncertainties that occur in real-time. Algorithms are proposed in discrete-time that combine adaptive approaches proposed for parameter and output estimation and ML approaches proposed for accelerated performance that guarantee stable estimation even in the presence of time-varying regressors, and for accelerated learning of the parameters with persistent excitation. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform and benchmark problems in ML. All results clearly point out the advantage of adaptive approaches for real-time control and learning.
翻译:本文件探讨了在面临不确定性的动态系统中实时控制和学习的问题。建议采用适应性办法解决这一问题,同时结合加强学习和机械学习的方法和工具。建议采用连续时间进行调整,结合适应性办法和RL进行在线控制政策,保证在存在实时存在的参数不确定性的情况下稳定行为。建议采用离散时间进行调整,其中结合为参数和产出估计提议的适应性办法和为加速性能而提议的调整性办法,以确保即使在时间变化递减者在场的情况下也进行稳定的估算,并加速以持续推理的方式学习参数。所有算法的量化验证工作都是在移动的平台上进行,基准问题在ML都存在。所有结果都清楚地表明了实时控制和学习的适应性办法的优势。