Advancements in artificial intelligence are leading researchers to find use cases that were
not as straightforward to solve in the past. The use case of simulated autonomous driving …

This paper presents a novel fixed-time adaptive Fault Tolerant Control (FTC) framework for
MIMO nonlinear Euler-Lagrange systems using sliding mode-based strategy, reinforcement …

This comprehensive review article delves into the intricate realm of fault-tolerant control
(FTC) schemes tailored for robotic manipulators. Our exploration spans the historical …

We propose a method to capture the handling abilities of fast jet pilots in a software model
via reinforcement learning (RL) from human preference feedback. We use pairwise …

This paper presents a fixed-time adaptive resilient control framework based on
reinforcement learning (RL), mismatch disturbance observer and nonsingular fast terminal …

Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years.
However, centralized critics in state-of-the-art MAPG methods still face the centralized …

In this paper, we investigate the optimal control strategies for model‐free zero‐sum games
involving the H∞ H _ ∞ control. The key contribution is the development of a Q‐learning …

Vehicle simulation algorithms play a crucial role in enhancing traffic efficiency and safety by
predicting and evaluating vehicle behavior in various traffic scenarios. Recently, vehicle …

This thesis focuses on the application of reinforcement learning (RL) techniques to design
optimal controllers and observers for linear time-invariant (LTI) systems, namely linear …

As progress in AI impacts all sectors of society, the world is destined to see increasingly
complex and numerous autonomous decision-making agents, which act upon their …