Bounded-Error LQR-Trees
Bounded-Error LQR-Trees
复制标题
有界误差 LQR 树
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
G. Konidaris
中科院分区：
文献类型：
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作者：
Barrett Ames;G. Konidaris
我们提出了一种反馈运动规划算法--有界误差LQR-树，该算法利用强化学习理论来寻找误差有界的策略。该算法将局部有效的线性二次型调节器(LQR)组成一个非线性控制器，类似于LQR树构造策略的方法，但通过最小化在LQR控制器重叠区域估计的Bellman残差来最小化所构造策略的代价。我们证明了一个基于样本的真Bellman残差的上界，并且在一个简单的欠驱动的非线性系统上证明了比以前的方法减少了五倍的代价。
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the constructed policy by minimizing the Bellman Residual, which is estimated in the overlapping regions of LQR controllers. We prove a sample-based upper bound on the true Bellman Residual, and demonstrate a five-fold reduction in cost over previous methods on a simple underactuated nonlinear system.