YANN-RL initializes RL actor and critic networks with explicit multi-parametric linear MPC solutions via YANNs to start from linear optimal control performance and then learn nonlinear policies through online interaction.
AC4MPC: Actor-critic reinforcement learning for nonlinear model predictive control,
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A learned policy rollout is refined by one Newton step via Riccati recursion, yielding quadratic reduction in suboptimality for nonlinear MPC, shown on quadcopter trajectory tracking.
YANN-RL is tested on three PC-Gym chemical process case studies, showing reduced training time and near-NMPC performance compared to PPO, SAC, DDPG, and TD3.
citing papers explorer
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Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
YANN-RL initializes RL actor and critic networks with explicit multi-parametric linear MPC solutions via YANNs to start from linear optimal control performance and then learn nonlinear policies through online interaction.
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Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
A learned policy rollout is refined by one Newton step via Riccati recursion, yielding quadratic reduction in suboptimality for nonlinear MPC, shown on quadcopter trajectory tracking.
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Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
YANN-RL is tested on three PC-Gym chemical process case studies, showing reduced training time and near-NMPC performance compared to PPO, SAC, DDPG, and TD3.