Approxrl

ApproxRL: A Matlab Toolbox for Approximate RL and DP. This toolbox contains Matlab implementations of a number of approximate reinforcement learning (RL) and dynamic programming (DP) algorithms. Notably, it contains the algorithms used in the numerical examples from the book: L. Busoniu, R. Babuska, B. De Schutter, and D. Ernst, Reinforcement Learning and Dynamic Programming Using Function Approximators, CRC Press, Automation and Control Engineering Series. April 2010, 280 pages, ISBN 978-1439821084.


References in zbMATH (referenced in 28 articles )

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  1. Löschenbrand, Markus: Finding multiple Nash equilibria via machine learning-supported Gröbner bases (2020)
  2. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  3. Wang, Bin; Zhao, Dongbin; Cheng, Jin: Adaptive cruise control via adaptive dynamic programming with experience replay (2019)
  4. Bertsekas, Dimitri P.: Proximal algorithms and temporal difference methods for solving fixed point problems (2018)
  5. Leottau, David L.; Ruiz-del-Solar, Javier; Babuška, Robert: Decentralized reinforcement learning of robot behaviors (2018)
  6. Vamvoudakis, Kyriakos G.; Ferraz, Henrique: Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance (2018)
  7. Gemine, Quentin; Ernst, Damien; Cornélusse, Bertrand: Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution (2017)
  8. Vamvoudakis, Kyriakos G.: Q-learning for continuous-time linear systems: A model-free infinite horizon optimal control approach (2017)
  9. Vamvoudakis, Kyriakos G.; Mojoodi, Arman; Ferraz, Henrique: Event-triggered optimal tracking control of nonlinear systems (2017)
  10. Gaeta, Matteo; Loia, Vincenzo; Miranda, Sergio; Tomasiello, Stefania: Fitted Q-iteration by functional networks for control problems (2016)
  11. Panfili, Martina; Pietrabissa, Antonio; Oddi, Guido; Suraci, Vincenzo: A lexicographic approach to constrained MDP admission control (2016)
  12. Tutsoy, Onder: Design and comparison base analysis of adaptive estimator for completely unknown linear systems in the presence of OE noise and constant input time delay (2016)
  13. Tutsoy, Onder; Brown, Martin: Chaotic dynamics and convergence analysis of temporal difference algorithms with bang-bang control (2016)
  14. Fernandez-Gauna, Borja; Graña, Manuel; Lopez-Guede, Jose Manuel; Etxeberria-Agiriano, Ismael; Ansoategui, Igor: Reinforcement learning endowed with safe veto policies to learn the control of linked-multicomponent robotic systems (2015)
  15. Geramifard, Alborz; Dann, Christoph; Klein, Robert H.; Dabney, William; How, Jonathan P.: RLPy: a value-function-based reinforcement learning framework for education and research (2015) ioport
  16. Vamvoudakis, Kyriakos G.: Non-zero sum Nash Q-learning for unknown deterministic continuous-time linear systems (2015)
  17. Gaggero, Mauro; Gnecco, Giorgio; Sanguineti, Marcello: Approximate dynamic programming for stochastic (N)-stage optimization with application to optimal consumption under uncertainty (2014)
  18. Jung, Tobias; Wehenkel, Louis; Ernst, Damien; Maes, Francis: Optimized look-ahead tree policies: a bridge between look-ahead tree policies and direct policy search (2014)
  19. Laber, Eric B.; Lizotte, Daniel J.; Qian, Min; Pelham, William E.; Murphy, Susan A.: Dynamic treatment regimes: technical challenges and applications (2014)
  20. Lian, Chuanqiang; Xu, Xin; Zuo, Lei; Huang, Zhenhua: Adaptive critic design with graph Laplacian for online learning control of nonlinear systems (2014)

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