Before revision:  To that end, in theory, we formulate a constrained optimal control problem (OCP) for each candidate path, optimize them separately and choose the one with the best tracking performance to follow. 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:  To that end, in theory, we formulate a constrained optimal control problem (OCP) for each candidate path, optimize them separately and choose the one with the best tracking performance . To unload the heavy online computation , 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.