Reinforcement Learning for Mixed-Integer Problems Based on MPC

• URL: http://arxiv.org/abs/2004.01430v1
• Date: Fri, 3 Apr 2020 08:43:27 GMT
• Title: Reinforcement Learning for Mixed-Integer Problems Based on MPC
• Authors: Sebastien Gros, Mario Zanon
• Abstract summary: We present a policy approximation based on mixed-integer MPC schemes, and propose a computationally inexpensive technique to generate exploration in the mixed-integer input space. We then propose a simple compatible advantage function approximation for the proposed policy, that allows one to build the gradient of the mixed-integer MPC-based policy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model Predictive Control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning. This approach has been investigated for Q-learning and actor-critic methods, both in the context of nominal Economic MPC and Robust (N)MPC, showing very promising results. In that context, actor-critic methods seem to be the most reliable approach. Many applications include a mixture of continuous and integer inputs, for which the classical actor-critic methods need to be adapted. In this paper, we present a policy approximation based on mixed-integer MPC schemes, and propose a computationally inexpensive technique to generate exploration in the mixed-integer input space that ensures a satisfaction of the constraints. We then propose a simple compatible advantage function approximation for the proposed policy, that allows one to build the gradient of the mixed-integer MPC-based policy.

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