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Article overview
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A RL-based Policy Optimization Method Guided by Adaptive Stability Certification | | Shengjie Wang
; Fengbo Lan
; Xiang Zheng
; Yuxue Cao
; Oluwatosin Oseni
; Haotian Xu
; Yang Gao
; Tao Zhang
; | | Date: |
2 Jan 2023 | | Abstract: | In contrast to the control-theoretic methods, the lack of stability guarantee
remains a significant problem for model-free reinforcement learning (RL)
methods. Jointly learning a policy and a Lyapunov function has recently become
a promising approach to ensuring the whole system with a stability guarantee.
However, the classical Lyapunov constraints researchers introduced cannot
stabilize the system during the sampling-based optimization. Therefore, we
propose the Adaptive Stability Certification (ASC), making the system reach
sampling-based stability. Because the ASC condition can search for the optimal
policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC)
algorithm based on the ASC condition. Meanwhile, our algorithm avoids the
optimization problem that a variety of constraints are coupled into the
objective in current approaches. When evaluated on ten robotic tasks, our
method achieves lower accumulated cost and fewer stability constraint
violations than previous studies. | | Source: | arXiv, 2301.00521 | | Services: | Forum | Review | PDF | Favorites | |
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