Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

@article{Chen2021EnforcingPF,
  title={Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization},
  author={Bingqing Chen and Priya L. Donti and Kyri Baker and J. Zico Kolter and Mario Berg{\'e}s},
  journal={Proceedings of the Twelfth ACM International Conference on Future Energy Systems},
  year={2021}
}
While reinforcement learning (RL) is gaining popularity in energy systems control, its real-world applications are limited due to the fact that the actions from learned policies may not satisfy functional requirements or be feasible for the underlying physical system. In this work, we propose PROjected Feasibility (PROF), a method to enforce convex operational constraints within neural policies. Specifically, we incorporate a differentiable projection layer within a neural network-based policy… 

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