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RISadaptive optimal control of unknown constrained-input system using policy iteration and neural networks
This paper presents an online policy iteration (PI)
algorithm to learn the continuous-time optimal control solution
for unknown constrained-input systems. The proposed PI algorithm
is implemented on ...
TanDEM-X Large-Scale Study of Tropical Rainforests for Spaceborne SAR Calibration in X-band
Vision for road inspection
Reinforcement learning and neural networks for multi-agent nonzero-sum games of nonlinear constrained-input systems
This paper presents an online adaptive optimal control method based on reinforcement learning to solve the multi-agent nonzero-sum (NZS) differential games of nonlinear constrained-input continuous-time systems. A non-quadratic ...
Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems
In this paper, an integral reinforcement learning (IRL) algorithm on an actor–critic structure is developed
to learn online the solution to the Hamilton–Jacobi–Bellman equation for partially-unknown constrainedinp ...
A policy iteration approach to online optimal control of continuous-time constrained-input systems
This paper is an effort towards developing an online learning algorithm to find the optimal control
solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on
the policy iteration (PI...