On the use of backpropagation in associative reinforcement learning. - Ronald J. Williams (ICNN, 1988)

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks. - Ronald J. Williams; David Zipser (Neural Comput., 1989)

An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories. - Ronald J. Williams; Jing Peng (Neural Comput., 1990)

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning. - Ronald J. Williams (Mach. Learn., 1992)

Efficient Learning and Planning Within the Dyna Framework. - Jing Peng; Ronald J. Williams (Adapt. Behav., 1993)

Dynamic recurrent neural networks: Theory and applications. - C. Lee Giles; Gary M. Kuhn; Ronald J. Williams (IEEE Trans. Neural Networks Learn. Syst., 1994)

Incremental Multi-Step Q-Learning. - Jing Peng; Ronald J. Williams (Mach. Learn., 1996)

Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm. - Mohammad A. Al-Ansari; Ronald J. Williams (NIPS, 1998)

Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties. - Wenxu Tong; Ying Wei; Leonel F. Murga; Mary Jo Ondrechen; Ronald J. Williams (PLoS Comput. Biol., 2009)