A novel investigation on the effects of state and reward structure in designing deep reinforcement learning-based controller for nonlinear dynamical systems

In the last decade, the popularity of deep reinforcement learning (DRL)-based controller design for complex and uncertain nonlinear dynamic systems has grown exponentially due to its model-free approach. Most of these studies focus on algorithmic developments to improve the learning process. However...

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