SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards. - Sanjay Krishnan; Animesh Garg; Richard Liaw; Brijen Thananjeyan; Lauren Miller; Florian T. Pokorny; Ken Goldberg (WAFR, 2016)

HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards. - Sanjay Krishnan; Animesh Garg; Richard Liaw; Lauren Miller; Florian T. Pokorny; Ken Goldberg (CoRR, 2016)

Real-Time Machine Learning: The Missing Pieces. - Robert Nishihara; Philipp Moritz; Stephanie Wang; Alexey Tumanov; William Paul; Johann Schleier-Smith; Richard Liaw; Mehrdad Niknami; Michael I. Jordan; Ion Stoica (HotOS, 2017)

Iterative Noise Injection for Scalable Imitation Learning. - Michael Laskey; Jonathan Lee 002; Wesley Yu-Shu Hsieh; Richard Liaw; Jeffrey Mahler; Roy Fox; Ken Goldberg (CoRR, 2017)

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning. - Richard Liaw; Sanjay Krishnan; Animesh Garg; Daniel Crankshaw; Joseph E. Gonzalez; Ken Goldberg (CoRR, 2017)

Ray: A Distributed Framework for Emerging AI Applications. - Philipp Moritz; Robert Nishihara; Stephanie Wang; Alexey Tumanov; Richard Liaw; Eric Liang; William Paul; Michael I. Jordan; Ion Stoica (CoRR, 2017)

Ray RLLib: A Composable and Scalable Reinforcement Learning Library. - Eric Liang; Richard Liaw; Robert Nishihara; Philipp Moritz; Roy Fox; Joseph Gonzalez 001; Ken Goldberg; Ion Stoica (CoRR, 2017)

Benchmarks for reinforcement learning in mixed-autonomy traffic. - Eugene Vinitsky; Aboudy Kreidieh; Luc Le Flem; Nishant Kheterpal; Kathy Jang; Cathy Wu 002; Fangyu Wu 003; Richard Liaw; Eric Liang; Alexandre M. Bayen (CoRL, 2018)

RLlib: Abstractions for Distributed Reinforcement Learning. - Eric Liang; Richard Liaw; Robert Nishihara; Philipp Moritz; Roy Fox; Ken Goldberg; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (ICML, 2018)

Ray: A Distributed Framework for Emerging AI Applications. - Philipp Moritz; Robert Nishihara; Stephanie Wang; Alexey Tumanov; Richard Liaw; Eric Liang; Melih Elibol; Zongheng Yang; William Paul; Michael I. Jordan; Ion Stoica (OSDI, 2018)

Tune: A Research Platform for Distributed Model Selection and Training. - Richard Liaw; Eric Liang; Robert Nishihara; Philipp Moritz; Joseph E. Gonzalez; Ion Stoica (CoRR, 2018)

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline. - Richard Liaw; Romil Bhardwaj; Lisa Dunlap; Yitian Zou; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (SoCC, 2019)

SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards. - Sanjay Krishnan; Animesh Garg; Richard Liaw; Brijen Thananjeyan; Lauren Miller; Florian T. Pokorny; Ken Goldberg (Int. J. Robotics Res., 2019)

IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks. - Michael Luo; Jiahao Yao; Richard Liaw; Eric Liang; Ion Stoica (ICLR, 2020)

Elastic Hyperparameter Tuning on the Cloud. - Lisa Dunlap; Kirthevasan Kandasamy; Ujval Misra; Richard Liaw; Michael I. Jordan; Ion Stoica; Joseph E. Gonzalez (SoCC, 2021)

RubberBand: cloud-based hyperparameter tuning. - Ujval Misra; Richard Liaw; Lisa Dunlap; Romil Bhardwaj; Kirthevasan Kandasamy; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (EuroSys, 2021)

ESCHER: expressive scheduling with ephemeral resources. - Romil Bhardwaj; Alexey Tumanov; Stephanie Wang; Richard Liaw; Philipp Moritz; Robert Nishihara; Ion Stoica (SoCC, 2022)

REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale. - Richard Liaw; Paige Bailey; Ying Li; Maria Dimakopoulou; Yves Raimond (RecSys, 2022)