Effective Probabilistic Neural Networks Model for Model-Based Reinforcement Learning in USV

Engineering, Information Science, Computer Science, Automation,
Probabilistic Neural Networks   Model Predictive Control   Unmanned Surface Vehicles   Reinforcement Learning   Uncertainty Propagation  

A New Approach to Model Predictive Control for Unmanned Surface Vehicles (USV): A Probabilistic Neural Network-Based MBRL Framework Academic Background Unmanned Surface Vehicles (USVs) have seen rapid development in recent years within the field of marine science, finding extensive applications in scenarios such as marine transportation, environmen...

Self-Model-Free Learning versus Learning with External Rewards in Information-Constrained Environments

Information Science, Computer Science, Artificial Intelligence,
Reinforcement Learning   Information Loss   Adversarial Learning   Jamming Attacks   Autonomous Decision Making  

Self-Model-Free Learning vs. Learning with External Rewards in Information-Constrained Environments: A New Reinforcement Learning Framework In recent years, with the development of networks and artificial intelligence systems, networked learning mechanisms face significant security challenges. In the domain of reinforcement learning (RL), the loss ...

Optimal Control of Stochastic Markovian Jump Systems with Wiener and Poisson Noises: Two Reinforcement Learning Approaches

Mathematics, Engineering, Information Science, Computer Science, Artificial Intelligence,
Stochastic Markovian Jump Systems   Wiener Noise   Poisson Noise   Reinforcement Learning   Optimal Control   Policy Iteration Algorithm   Numerical Verification  

Optimal Control of Stochastic Markovian Jump Systems with Wiener and Poisson Noises: Two Reinforcement Learning Approaches Academic Context In modern control theory, optimal control is a crucial research field, aiming to design an optimal control strategy under various constraints for dynamic systems to minimize a given cost function. For stochasti...