• Corpus ID: 238253364

Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems

@article{Mahmud2021SafetyAM,
  title={Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems},
  author={S. M. Nahid Mahmud and Moad Abudia and Scott A. Nivison and Zachary I. Bell and Rushikesh Kamalapurkar},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.00271}
}
The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems are safety-critical. Safety requirements are often expressed in terms of state and/or control constraints. Methods such as barrier transformation and control barrier functions have been successfully used, in conjunction with model-based reinforcement learning, for safe learning in systems under state constraints, to… 
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Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning

Simulation results indicate that barrier transformation is an effective approach to achieve online reinforcement learning in safety-critical systems using output feedback.

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  • F. LewisK. Vamvoudakis
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
  • 2011
It is shown that, similar to Q-learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control.
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