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- W3207110310 abstract "The lack of stability guarantee restricts the practical use of learning-based methods in core control problems in robotics. We develop new methods for learning neural control policies and neural Lyapunov critic functions in the modelfree reinforcement learning (RL) setting. We use sample-based approaches and the Almost Lyapunov function conditions to estimate the region of attraction and invariance properties through the learned Lyapunov critic functions. The methods enhance stability of neural controllers for various nonlinear systems including automobile and quadrotor control." @default.
- W3207110310 created "2021-10-25" @default.
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- W3207110310 date "2021-05-30" @default.
- W3207110310 modified "2023-09-27" @default.
- W3207110310 title "Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics" @default.
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- W3207110310 doi "https://doi.org/10.1109/icra48506.2021.9560886" @default.
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