Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids

This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to efficiently solve finite-horizon optimal control problems in mixed-logical dynamical systems. Optimization-based control of such systems with discrete and continuous decision variables entails the online solution of mixed-integer quadratic or linear programs, which suffer from the curse of dimensionality. Our approach aims at mitigating this issue by effectively decoupling the decision on the discrete variables and the decision on the continuous variables. Moreover, to mitigate the combinatorial growth in the number of possible actions due to the prediction horizon, we conceive the definition of decoupled Q-functions to make the learning problem more tractable. The use of reinforcement learning reduces the online optimization problem of the MPC controller from a mixed-integer linear (quadratic) program to a linear (quadratic) program, greatly reducing the computational time. Simulation experiments for a microgrid, based on real-world data, demonstrate that the proposed method significantly reduces the online computation time of the MPC approach and that it generates policies with small optimality gaps and high feasibility rates.

Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of highway ramp metering control that embeds Reinforcement Learning techniques within the Model Predictive Control framework. The control problem is formulated as an RL task by crafting a suitable stage cost function that is representative of the traffic conditions, variability in the control action, and violations of a safety-critical constraint on the maximum number of vehicles in queue. An MPC-based RL approach, which merges the advantages of the two paradigms in order to overcome the shortcomings of each framework, is proposed to learn to efficiently control an on-ramp and to satisfy its constraints despite uncertainties in the system model and variable demands. Finally, simulations are performed on a benchmark from the literature consisting of a small-scale highway network. Results show that, starting from an MPC controller that has an imprecise model and is poorly tuned, the proposed methodology is able to effectively learn to improve the control policy such that congestion in the network is reduced and constraints are satisfied, yielding an improved performance compared to the initial controller.