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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22267 |
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| _version_ | 1866909870044741632 |
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| author | Li, Weijian Malikopoulos, Andreas A. |
| author_facet | Li, Weijian Malikopoulos, Andreas A. |
| contents | In this paper, we investigate the infinite-horizon risk-constrained linear quadratic regulator problem (RC-QR), which augments the classical LQR formulation with a statistical constraint on the variability of the system state to incorporate risk awareness, a key requirement in safety-critical control applications. We propose an actor-critic learning algorithm that jointly performs policy evaluation and policy improvement in a model-free and online manner. The RC-QR problem is first reformulated as a max-min optimization problem, from which we develop a multi-time-scale stochastic approximation scheme. The critic employs temporal-difference learning to estimate the action-value function, the actor updates the policy parameters via a policy gradient step, and the dual variable is adapted through gradient ascent to enforce the risk constraint. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Actor-Critic Learning for Risk-Constrained Linear Quadratic Regulation Li, Weijian Malikopoulos, Andreas A. Optimization and Control In this paper, we investigate the infinite-horizon risk-constrained linear quadratic regulator problem (RC-QR), which augments the classical LQR formulation with a statistical constraint on the variability of the system state to incorporate risk awareness, a key requirement in safety-critical control applications. We propose an actor-critic learning algorithm that jointly performs policy evaluation and policy improvement in a model-free and online manner. The RC-QR problem is first reformulated as a max-min optimization problem, from which we develop a multi-time-scale stochastic approximation scheme. The critic employs temporal-difference learning to estimate the action-value function, the actor updates the policy parameters via a policy gradient step, and the dual variable is adapted through gradient ascent to enforce the risk constraint. |
| title | Actor-Critic Learning for Risk-Constrained Linear Quadratic Regulation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.22267 |