Application of Newton's Method to Action Selection in Continuous State- and Action-Space Reinforcement Learning
Barry D. Nichols, Dimitris C. Dracopoulos · Middlesex University Research Repository (Middlesex University Of London) · 2014
Abstract. An algorithm based on Newton’s Method is proposed for ac-tion selection in continuous state- and action-space reinforcement learning without a policy network or discretization. The proposed method is val-idated 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. 1