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This paper introduces an Adaptive Stochastic Nonlinear Model Predictive Control (aSNMPC) framework that utilizes Deep Reinforcement Learning (RL) to optimize motion control for autonomous vehicles. The RL agent dynamically adjusts key SNMPC parameters, improving robustness and feasibility in the presence of uncertainties and disturbances. Experimental results demonstrate that the aSNMPC outperforms traditional SNMPC approaches, particularly in challenging driving scenarios and previously unexplored environm

Author

sasukecharingan41

Language

EN