Robards, MatthewSunehag, Peter2025-12-312025-12-3197836422994520302-9743https://hdl.handle.net/1885/733797391We introduce and empirically evaluate two novel online gradient-based reinforcement learning algorithms with function approximation - one model based, and the other model free. These algorithms come with the possibility of having non-squared loss functions which is novel in reinforcement learning, and seems to come with empirical advantages. We further extend a previous gradient based algorithm to the case of full control, by using generalized policy iteration. Theoretical properties of these algorithms are studied in a companion paper.12en201210.1007/978-3-642-29946-9_784861701646