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Learning the optimal state-feedback via supervised imitation learning

Astrodynamics 2019年第4期3卷 361-374页

作者： dharmesh tailor Dario Izzo Advanced Concepts Team European Space AgencyNoondwijk2201 AZthe Netherlands

Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert *** substituting expert demonstrations for optimal behaviours,the same paradigm leads to the design of control policies closely approximating the optimal *** approach requires training a machine learning algorithm(in our case deep neural networks)directly on state-control pairs originating from optimal *** have shown in previous work that,when restricted to low-dimensional state and control spaces,this approach is very successful in several deterministic,non-linear problems in *** this work,we refine our previous studies using as a test case a simple quadcopter model with quadratic and time-optimal objective *** describe in detail the best learning pipeline we have developed,that is able to approximate via deep neural networks the state-feedback map to a very high *** introduce the use of the softplus activation function in the hidden units of neural networks showing that it results in a smoother control profile whilst retaining the benefits of *** show how to evaluate the optimality of the trained state-feedback,and find that already with two layers the objective function reached and its optimal value differ by less than one *** later consider also an additional metric linked to the system asymptotic behaviour-time taken to converge to the policy’s fixed *** respect to these metrics,we show that improvements in the mean absolute error do not necessarily correspond to better policies.

关键词： optimal control deep learning imitation learning G&CNET

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