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Found 9 papers, 2 papers with code
Data-Driven Hamiltonian for Direct Construction of Safe Set from Trajectory Data
The reachable set computed based on the DDH is also ensured to be a conservative approximation of the true reachable set.
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning to enhance the flexibility of error distribution modeling by incorporating additional higher-order moment, particularly kurtosis, thereby improving the estimation and mitigation of data-dependent aleatoric uncertainty.
Synchronisation-Oriented Design Approach for Adaptive Control
In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model.
Automatic Optimisation of Normalised Neural Networks
The first algorithm utilises automatic differentiation of the objective function along the update curve defined on the combined manifold of spheres.
Dynamic deep-reinforcement-learning algorithm in Partially Observed Markov Decision Processes
Reinforcement learning has been greatly improved in recent studies and an increased interest in real-world implementation has emerged in recent years.
A Passivity-Based Method for Accelerated Convex Optimisation
This study presents a constructive methodology for designing accelerated convex optimisation algorithms in continuous-time domain.
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables.
Bayesian Learning Approach to Model Predictive Control
High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control.
Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient
This study presents a policy optimisation framework for structured nonlinear control of continuous-time (deterministic) dynamic systems.
