Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems
- 1 January 2014
- journal article
- Published by Elsevier BV in Automatica
- Vol. 50 (1), 193-202
- https://doi.org/10.1016/j.automatica.2013.09.043
Abstract
No abstract availableThis publication has 16 references indexed in Scilit:
- A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systemsAutomatica, 2013
- Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet lossesAutomatica, 2012
- Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problemAutomatica, 2010
- Real-time reinforcement learning by sequential Actor–Critics and experience replayNeural Networks, 2009
- Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systemsNeural Networks, 2009
- Adaptive optimal control for continuous-time linear systems based on policy iterationAutomatica, 2009
- Batch reinforcement learning in a complex domainPublished by Association for Computing Machinery (ACM) ,2007
- Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approachAutomatica, 2005
- Adaptive dynamic programmingIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2002
- Self-improving reactive agents based on reinforcement learning, planning and teachingMachine Learning, 1992