English and Cantonese
My main research interests are in computational intelligence, including reinforcement learning; artificial neural networks and evolutionary algorithms. My recent research investigates the application of these techniques to automatic generation of solutions to continuous action-space control problems.
Nichols, Barry D. (2017) A comparison of eligibility trace and momentum on SARSA in continuous state- and action-space. In: 9th Computer Science & Electronic Engineering Conference (CEEC 2017), 27-29 Sep 2017, Colchester, UK.
Dracopoulos, Dimitris C. and Nichols, Barry D. (2017) Genetic programming for the minimum time swing up and balance control acrobot problem. Expert Systems , 34 (5). ISSN 1468-0394
Nichols, Barry D. (2016) A comparison of action selection methods for implicit policy method reinforcement learning in continuous action-space. In: International Joint Conference on Neural Networks (IJCNN 2016), 24-29 Jul 2016, Vancouver, Canada.
Nichols, Barry D. (2015) Continuous action-space reinforcement learning methods applied to the minimum-time swing-up of the acrobot. In: Systems, Man and Cybernetics (SMC), 2015 IEEE International Conference on. Institute of Electrical and Electronics Engineers (IEEE), pp. 2084-2089. ISBN 9781479986965
Nichols, Barry D. and Dracopoulos, Dimitris C. (2014) Application of Newton's method to action selection in continuous state- and action-space reinforcement learning. In: ESANN 2014 Proceedings: 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges April 23-24-25, 2014. ESANN, pp. 141-146. ISBN 9782874190957
2012 Best refereed application paper at AI-2012 Thirty-Second SGAI International Conference on Artificial Intelligence
International Joint Conference on Neural Networks (IJCNN)
BCS Advanced Programming Specialist Group