|Chapter title||Application of Newton's Method to action selection in continuous state- and action-space reinforcement learning|
|Authors||Nichols, B.D. and Dracopoulos, D.|
An algorithm based on Newton's Method is proposed for action selection in continuous state- and action-space reinforcement learning without a policy network or discretization. The proposed method is validated on two benchmark problems: Cart-Pole and double Cart-Pole on which the proposed method achieves comparable or improved performance with less parameters to tune and in less training episodes than CACLA, which has previously been shown to outperform many other continuous state- and action-space reinforcement learning algorithms.
|Book title||ESANN 2014 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 23-25 April 2014|
|Web address (URL)||http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-175.pdf|
|Journal||European Symposium on Artificial Neural Networks|