基于探索式同步积分强化学习的在线自适应最优控制算法
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations
摘要：
本文提出了一种基于同步策略迭代的同步积分Q学习算法，用于求解输入仿射系统动态连续时间无限时域最优控制问题。在该方法中，整体钢筋被测量为激励信号，以估计Hamilton-Jacobi-Bellman方程的解。此外，所提出的方法是完全无模型的，即不需要系统的先验知识。通过策略迭代，执行器和批评神经网络可以同时逼近最优值函数和策略。为了保证两个网络的收敛性，需要激励条件的持续性。与传统的策略迭代算法不同，该方法放宽了对初始容许策略的限制。通过数值仿真验证了该算法的有效性。
英文摘要：
In this paper, we present a novel algorithm named synchronous integral Q-learning, which is based on synchronous policy iteration, to solve the continuous-time infinite horizon optimal control problems of input-affine system dynamics. The integral reinforcement is measured as an excitation signal in this method to estimate the solution to the Hamilton-Jacobi-Bellman equation. Moreover, the proposed method is completely model-free, i.e. no a priori knowledge of the system is required. Using policy iteration, the actor and critic neural networks can simultaneously approximate the optimal value function and policy. The persistence of excitation condition is required to guarantee the convergence of the two networks. Unlike in traditional policy iteration algorithms, the restriction of the initial admissible policy is relaxed in this method. The effectiveness of the proposed algorithm is verified through numerical simulations.
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