Learning Action Representations for Reinforcement Learning. - Yash Chandak; Georgios Theocharous; James E. Kostas; Scott M. Jordan; Philip S. Thomas (ICML, 2019)

Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock. - James E. Kostas; Chris Nota; Philip S. Thomas (CoRR, 2019)

Classical Policy Gradient: Preserving Bellman's Principle of Optimality. - Philip S. Thomas; Scott M. Jordan; Yash Chandak; Chris Nota; James E. Kostas (CoRR, 2019)

Asynchronous Coagent Networks. - James E. Kostas; Chris Nota; Philip S. Thomas (ICML, 2020)

High Confidence Generalization for Reinforcement Learning. - James E. Kostas; Yash Chandak; Scott M. Jordan; Georgios Theocharous; Philip S. Thomas (ICML, 2021)

Structural Credit Assignment in Neural Networks using Reinforcement Learning. - Dhawal Gupta; Gabor Mihucz; Matthew Schlegel; James E. Kostas; Philip S. Thomas; Martha White (NeurIPS, 2021)

Edge-Compatible Reinforcement Learning for Recommendations. - James E. Kostas; Philip S. Thomas; Georgios Theocharous (CoRR, 2021)

Seldonian Toolkit: Building Software with Safe and Fair Machine Learning. - Austin Hoag; James E. Kostas; Bruno Castro da Silva; Philip S. Thomas; Yuriy Brun (ICSE Companion, 2023)

Coagent Networks: Generalized and Scaled. - James E. Kostas; Scott M. Jordan; Yash Chandak; Georgios Theocharous; Dhawal Gupta; Martha White; Bruno Castro da Silva; Philip S. Thomas (CoRR, 2023)

The Cliff of Overcommitment with Policy Gradient Step Sizes. - Scott M. Jordan; Samuel Neumann; James E. Kostas; Adam White 001; Philip S. Thomas (RLJ, 2024)