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A novel composite-gradient learning (CGL) method is proposed that integrates the MPC controller into the learning process by representing the DRL and MPC control inputs as a joint action and accounting for their interaction when updating the DRL agent during training.
It is shown that the state distribution of the system can be tractably approximated over time by Gaussian mixture distributions, with formal error bounds in Wasserstein distance, which yields closed-form expressions for expected costs and chance constraints.
A novel DRL-MPC framework for multi-class transportation networks that divides control authority between DRL and MPC is proposed, combining DRL's fast online computation and model independence with MPC's built-in optimization and constraint-handling capabilities.
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