Before revision:  More importantly , we propose a model-based reinforcement learning (RL) algorithm , which is served as an approximate constrained OCP solver , to unload the heavy computation by the paradigm of offline training and online application . Specifically, the OCPs for all paths are considered together to construct a multi-task RL problem and then solved offline by our algorithm into value and policy networks, for real-time online path selecting and tracking respectively.
After revision:  More importantly , we propose a model-based reinforcement learning (RL) algorithm , which is served as an approximate constrained OCP solver , to unload the heavy computation by the paradigm of offline training and online application . Specifically, the OCPs for all paths are considered together to construct a multi-task RL problem and then solved offline in the form of value and policy networks, for real-time online path selecting and tracking respectively.