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Found 8 papers, 2 papers with code
Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States (Extended Version)
Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrarily given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved.
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set
This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and action-dependent uncertainty.
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
We also show that a modified version of regularized policy iteration, i. e., with finite-step policy evaluation, is equivalent to inexact Newton method where the Newton iteration formula is solved with truncated iterations.
Robust Safe Reinforcement Learning under Adversarial Disturbances
To address this challenge, this paper proposes a robust safe reinforcement learning framework that tackles worst-case disturbances.
Safe Reinforcement Learning with Dual Robustness
To address this issue, we propose a systematic framework to unify safe RL and robust RL, including problem formulation, iteration scheme, convergence analysis and practical algorithm design.
Real-time detection of abnormal driving behavior based on long short-term memory network and regression residuals
Second, a time windowbased residual algorithm is designed and employed to detect abnormal driving behavior according to the magnitude and continuity of the residuals.
Smoothing Policy Iteration for Zero-sum Markov Games
In this paper, we propose the smoothing policy iteration (SPI) algorithm to solve the zero-sum MGs approximately, where the maximum operator is replaced by the weighted LogSumExp (WLSE) function to obtain the nearly optimal equilibrium policies.
Scalable Quantum Tomography with Fidelity Estimation
We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states.
Quantum Physics
