A litmus test based on reachset-conformant model identification and correlation analysis of uncertainties predicts if RL-based control is superior to model-based control without any RL training.
Neuronlike adaptive elements that can solve difficult learning control problems,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.
The authors model gradient descent for optimal control as a separate dynamical system embedded in the plant dynamics to enable reachability-based verification of autonomous systems.
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.
citing papers explorer
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To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
A litmus test based on reachset-conformant model identification and correlation analysis of uncertainties predicts if RL-based control is superior to model-based control without any RL training.
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Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.
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Verification of Autonomous Systems with Optimal Controllers
The authors model gradient descent for optimal control as a separate dynamical system embedded in the plant dynamics to enable reachability-based verification of autonomous systems.
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Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.